Smart-meter data is sensitive, and its centralization raises privacy concerns. Federated learning and privacy-preserving techniques promise to enable analytics without compromising user confidentiality. The practical viability of these methods in real-world smart-grid applications remains uncertain. A critical synthesis of their limitations, privacy assumptions, and deployment gaps is still needed. This review critically evaluates the state-of-the-art in federated and privacy-preserving learning for smart-meter analytics, grid-edge control, and DER coordination. It focuses on both methodological promise and deployment realism. A critical review of 27 peer-reviewed articles from 2017 to 2025 is conducted. The assessment focuses on technical performance, privacy guarantees, scalability, security, and deployment readiness. The review identifies key strengths in theoretical privacy and decentralized learning. It also finds substantial limitations in communication efficiency, non-IID robustness, adversarial resilience, and energy-sector-specific feasibility. Federated and privacy-preserving learning holds great promise, but critical technical and regulatory barriers must be overcome. The review provides a roadmap for future interdisciplinary research.
Smart-meter data provides fine-grained visibility into household and grid-edge behavior, but the same granularity makes it deeply privacy-sensitive. Differential privacy studies using real smart-meter data show that consumption traces can reveal behavioral patterns even before advanced learning models are applied [1]. The literature shows that this privacy challenge is not peripheral to energy analytics but structurally embedded in load forecasting, theft detection, demand response, and consumer profiling [2]. A critical review is therefore needed because privacy-preserving learning is often presented as a solution before its assumptions are tested under realistic grid conditions.
Federated learning has emerged as a leading approach because it allows models to be trained across distributed meters, homes, or utilities without directly pooling raw data. Early smart-grid applications used federated architectures to identify consumer characteristics while keeping data local, demonstrating the conceptual fit between decentralized learning and decentralized electricity use [2]. Residential load forecasting studies further show that federated recurrent and personalized models can exploit cross-household learning while reducing the need for centralized smart-meter repositories [3, 4]. However, this review finds that decentralization alone does not guarantee privacy, robustness, or deployability.
Privacy-preserving smart-grid learning has also expanded beyond federated learning to include differential privacy, secure aggregation, secure multi-party computation, and homomorphic encryption. Differentially private optimal power flow and renewable-energy smart-metering studies indicate that formal privacy guarantees can be integrated into power-system optimization, but often with accuracy or operational-efficiency costs [5, 6]. Secure federated detection of false data injection attacks shows that privacy and security objectives may overlap, yet they can also compete when encrypted or noisy updates reduce situational awareness [7]. The literature therefore requires critical assessment rather than simple classification of techniques.
The need for review is amplified by the growing role of grid-edge intelligence, flexible demand, electric vehicles, storage, and distributed energy resources. Federated reinforcement learning has been proposed for smart-home energy management with distributed resources, suggesting that privacy-preserving learning is moving from offline analytics toward control [8]. More recent work on edge intelligence for smart meters illustrates the ambition to embed learning directly into distributed metering infrastructure [9]. Yet few studies fully address the latency, cyber-security, regulatory, and accountability requirements of real distribution-system operation.
This review covers federated learning, differential privacy, secure aggregation, secure multi-party computation, and homomorphic encryption when they are applied to smart-meter analytics, grid-edge control, or DER coordination. It includes load forecasting, consumer profiling, theft and anomaly detection, demand response baseline estimation, EV charging coordination, battery and flexible-load aggregation, and peer-to-peer energy trading. Studies on federated smart-meter forecasting, privacy-preserving theft detection, and federated data sharing are included because they directly address decentralized learning over sensitive energy data [3, 10, 11]. The review excludes purely centralized forecasting studies, non-peer-reviewed work, and methods that do not address privacy, decentralization, or grid-edge deployment.
The article is a critical review rather than a systematic meta-analysis or experimental benchmark. It evaluates how the selected literature frames privacy guarantees, performance trade-offs, scalability, communication efficiency, and real-world feasibility, including works on asynchronous federated learning, federated split learning, and heterogeneous federated learning [9, 12, 13]. Regulatory and compliance issues are considered only where they affect deployment readiness, data access, or energy-sector privacy governance. The review does not introduce new empirical results, simulated benchmarks, or new mathematical protocols.
Federated learning trains a shared model across distributed clients by exchanging model parameters or updates instead of raw data, which makes it attractive for smart-meter environments. In residential load forecasting, this paradigm allows households or utility zones to contribute to a global model while retaining local consumption traces [3]. The literature shows that this architecture can reduce direct data centralization risks, but it does not remove privacy leakage from gradients, model updates, or client participation patterns [4]. A key limitation is that many energy studies still equate local data retention with complete privacy protection.
Privacy-preserving enhancements attempt to address the leakage risks that remain after federated learning decentralizes data storage. Differential privacy introduces calibrated noise to limit the influence of individual records, and smart-grid studies show its usefulness for both metering data and optimization settings [1, 5]. Secure aggregation and related cryptographic methods can hide individual updates from the server, while privacy-preserving federated theft-detection studies show that such protections are increasingly viewed as necessary rather than optional [14]. However, this review finds that stronger privacy guarantees often increase computational burden, reduce accuracy, or complicate deployment on constrained edge devices.
Distributed energy systems are structurally aligned with federated learning because data, control authority, and physical assets are already dispersed across homes, feeders, aggregators, and devices. Federated reinforcement learning for multiple smart homes demonstrates how local energy agents can learn coordinated behavior without full data centralization [8]. Demand-response baseline estimation further illustrates the relevance of horizontal federated learning when customers or aggregators possess fragmented but complementary consumption records [15]. The critical challenge is that power systems require reliable and timely operation, while federated learning often assumes communication rounds and convergence conditions that may not match grid-control time scales.
Figure 1 illustrates how federated and privacy-preserving learning methods progress from distributed smart-meter and DER data environments toward operational deployment, while exposing the privacy, communication, robustness, scalability, and governance constraints identified in the review.
Figure 1. Privacy-Preserving Learning-to-Deployment Readiness Architecture for Smart-Meter Analytics, Grid-Edge Control, and DER Coordination
Load forecasting is one of the most mature applications of federated smart-meter analytics, with studies showing that recurrent, personalized, and asynchronous models can learn from distributed household data [3, 12]. Privacy-preserving residential forecasting demonstrates clear value because individual consumption profiles can remain local while aggregated model knowledge improves prediction [4]. Personalized federated load prediction also addresses the fact that household demand is heterogeneous, but it introduces questions about fairness, model drift, and how much personalization weakens the benefits of federation [16]. The literature shows promise, yet most evaluations remain limited to historical datasets rather than live operational forecasting pipelines.
Anomaly, false-data, and energy-theft detection studies show that federated learning is increasingly used for security-sensitive smart-grid analytics. FedDetect applies a privacy-preserving federated framework to energy-theft detection, indicating that distributed detection can reduce raw-data exposure while still identifying suspicious consumption behavior [10]. Secure federated deep learning for false data injection attacks extends this logic to cyber-physical grid security, where the detection target is not only consumer fraud but also malicious manipulation of grid measurements [7]. A key limitation is that adversaries may attack the federated process itself, which means privacy-preserving detection systems must also be robust against poisoning, evasion, and inference threats.
Consumer segmentation and profiling highlight a tension at the center of privacy-preserving smart-meter analytics: the more useful the analytics, the more revealing the inferred categories may become. Federated consumer-characteristics identification shows that collaborative learning can classify or characterize electricity users without centralizing raw readings [2]. This is methodologically important, but it also raises a critical ethical issue because privacy risk can shift from raw data exposure to inferred behavioral labels. The review therefore finds that privacy-preserving profiling must be assessed not only by data-locality claims but also by the sensitivity of the outputs it produces.
The comparative literature indicates that no single privacy-preserving technique dominates across accuracy, overhead, and privacy guarantee. Differential privacy provides formal guarantees but can degrade performance when noise is high, especially in fine-grained metering or control applications [1]. Federated learning reduces centralized data collection but remains vulnerable unless combined with secure aggregation, robust aggregation, or privacy accounting, as shown by privacy-preserving theft-detection and communication-efficient prediction schemes [14, 17]. Table 1 summarizes the main privacy techniques used or implied in the reviewed smart-meter analytics literature and highlights their practical trade-offs.
Table 1. Privacy Techniques for Smart-Meter Analytics
Privacy technique | Typical smart-meter use | Accuracy impact | Computational overhead | Privacy guarantee | Critical assessment |
Federated learning | Load forecasting, profiling, theft detection, false-data detection | Usually moderate when data are sufficiently similar, but weaker under non-IID household behavior | Moderate communication and client-training burden | Keeps raw data local but does not by itself prevent update leakage | Strong practical appeal, but privacy is often overstated when no secure aggregation or inference defense is used |
Differential privacy | Smart-meter data release, demand response, optimal power flow, privacy-aware analytics | Can be significant when strict privacy budgets require high noise | Low to moderate computational cost, but tuning privacy budgets is difficult | Formal record-level or user-level privacy depending on mechanism design | Strong theoretical protection, but utility loss and unclear energy-sector privacy-budget norms limit deployment |
Secure multi-party computation | Collaborative analytics or aggregation without revealing individual inputs | Usually low direct accuracy loss because data are protected cryptographically rather than noised | High communication and synchronization overhead | Strong cryptographic protection under specified threat models | Powerful for aggregation, but often difficult to scale to millions of meters and low-latency control |
Homomorphic encryption | Encrypted inference or aggregation over sensitive metering inputs | Usually low direct accuracy loss, though approximations may affect complex models | High computational cost, especially for deep learning and edge devices | Allows computation on encrypted data under cryptographic assumptions | Attractive for high-sensitivity use cases, but currently constrained by latency, device capability, and operational complexity |
Grid-edge control requires analytics to move from retrospective prediction toward decisions that affect voltage, frequency, and local balancing. Differentially private optimal power flow shows that privacy-preserving methods can be embedded in grid optimization, but it also demonstrates that privacy protection may alter solution quality and operational feasibility [5]. Risk assessment based on federated learning extends the role of privacy-preserving AI toward operational decision support, where distributed data can inform grid risk without full centralization [18]. Few studies, however, validate whether these methods can satisfy the reliability expectations of real-time distribution automation.
Real-time edge inference is essential because smart meters, gateways, and local controllers cannot always wait for centralized cloud processing. Federated split learning for smart meters directly targets this problem by distributing intelligence between edge devices and higher-level infrastructure [9]. The literature shows that such architectures may reduce bandwidth and protect raw data, but they also introduce dependencies between device capacity, partitioned model design, and communication stability. A key limitation is that many studies demonstrate inference feasibility without fully evaluating maintenance, software updates, fault recovery, and cyber-physical safety.
Communication is one of the strongest practical constraints on federated learning at the grid edge. Communication-efficient energy prediction schemes recognize that repeated model-update exchange can burden smart-grid communication networks, especially when the number of meters or clients grows [17]. Asynchronous adaptive federated learning attempts to reduce synchronization bottlenecks in distributed load forecasting, which is important because meters and edge devices may be intermittently connected or resource constrained [12]. This review finds that communication efficiency is often treated as a model-training issue, while its interaction with utility network operations and service-level requirements remains underexplored.
Grid-edge control exposes privacy-utility trade-offs more sharply than offline analytics because control decisions must be accurate, timely, and explainable. Privacy-preserving false-data detection can protect distributed measurements, but excessive noise, encryption latency, or incomplete client participation may weaken situational awareness [19]. Federated energy management in smart homes shows that decentralized learning can support privacy-aware control, yet performance depends on the representativeness of local agents and the stability of the environment [8]. The literature therefore supports privacy-preserving grid-edge intelligence in principle, but real-world feasibility depends on explicit accounting for latency, cyber-security, regulatory accountability, and operational fallback mechanisms.
EV charging coordination is a natural application for federated and privacy-preserving learning because charging profiles reveal mobility, occupancy, and socioeconomic patterns. Federated prediction of EV charging load shows that decentralized learning can support charging-demand estimation while reducing the need to centralize user-level charging records [20]. However, this review finds that prediction alone is insufficient for coordination because operational charging control also requires constraints, feeder limits, tariffs, and user preferences to be represented reliably. A key limitation is that few EV-focused studies connect federated learning outputs to validated real-time charging decisions under distribution-network constraints.
Battery storage aggregation requires coordination across many small devices whose state-of-charge, availability, and cycling preferences may be commercially and personally sensitive. Federated energy optimization frameworks suggest that local devices can participate in shared learning objectives without directly exposing operational data, which is relevant for aggregating behind-the-meter storage [21]. Personalized federated household load prediction further indicates that device-level heterogeneity cannot be ignored when batteries are coordinated through learned forecasts [22]. The literature shows promise, but it rarely evaluates battery degradation, aggregator incentives, or conflicting objectives between customer value and grid services.
Demand response is one of the strongest cases for privacy-preserving DER coordination because it depends on flexible consumption while exposing detailed household behavior. Adaptive horizontal federated learning for demand-response baseline estimation demonstrates that privacy-aware learning can support customer-specific baselines without fully centralizing consumption histories [15]. Differential privacy for renewable-energy smart metering also shows that privacy mechanisms can be connected to distributed energy participation, although utility loss remains a central concern [6]. This review finds that demand-response studies often understate the difficulty of verifying performance, detecting gaming, and allocating incentives when the underlying data remain distributed or perturbed.
Multi-agent DER coordination extends privacy-preserving learning from prediction to strategic interaction among homes, aggregators, prosumers, and market platforms. Federated reinforcement learning for decentralized peer-to-peer energy trading shows how agents can learn trading behavior without centralizing all participant data [23]. Integrated blockchain and federated learning for DER cybersecurity further suggests that privacy-preserving coordination may need tamper-resistance, auditability, and cyber-defense mechanisms beyond model training alone [24]. A key limitation is that multi-agent learning can amplify instability, strategic manipulation, and fairness concerns when participants have unequal data quality, device flexibility, or market power.
Communication overhead remains one of the most persistent limitations in federated smart-grid learning. Residential federated load forecasting can require repeated exchange of model updates, and even efficient implementations may become burdensome as client numbers increase [25]. Communication-efficient energy prediction explicitly addresses this issue, but its optimization of message exchange does not fully solve latency requirements for protection, control, or fast DER dispatch [17]. This review finds that many studies evaluate communication cost as bandwidth or rounds while giving less attention to utility communication reliability, outage conditions, and operational prioritization.
Non-IID data are not an exception in smart-meter analytics but the normal condition because households differ in appliances, occupancy, climate exposure, tariffs, and DER ownership. Personalized federated learning for consumer load forecasting shows that customization can improve performance when generic global models fail to capture individual patterns [16]. Heterogeneous federated learning for electricity theft detection similarly shows that class imbalance and client heterogeneity must be handled explicitly rather than treated as minor dataset imperfections [13]. A key limitation is that personalization can reduce comparability, complicate privacy accounting, and make system-wide validation harder.
Federated learning can reduce raw-data exposure while creating new attack surfaces through model updates, aggregation protocols, and client participation. A privacy-preserving federated learning scheme against poisoning attacks shows that poisoning is a serious concern in smart-grid deployments where compromised clients may manipulate model behavior [26]. Secure false-data injection detection studies highlight that cyber-security and privacy must be jointly considered because attackers may target both the grid measurements and the learning pipeline [7]. This review finds that few studies provide end-to-end threat models covering inference attacks, poisoning, backdoors, compromised aggregators, and malicious edge devices simultaneously.
A major weakness of the literature is the lack of standardized benchmarks that jointly evaluate forecasting accuracy, privacy leakage, communication cost, robustness, and deployment feasibility. Studies on smart-meter data sharing, theft detection, forecasting, and risk assessment often use different datasets, privacy assumptions, and evaluation metrics, which makes cross-study comparison difficult [11]. Broader privacy-preservation reviews for smart meters emphasize that the field lacks consistent ways to compare privacy mechanisms under realistic household and grid conditions [27]. Table 2 synthesizes the critical limitations identified across the reviewed literature and links them to representative references.
Table 2. Critical Limitations and Associated Literature
Critical limitation | How it appears in the literature | Why it matters for deployment | Representative references |
Communication cost | Repeated federated rounds, client synchronization, and update transmission burden smart-grid communication networks | Large-scale smart-meter fleets may not support frequent model exchange during congestion, outages, or low-bandwidth operation | |
Non-IID and imbalanced data | Household behavior, theft labels, EV charging, and DER flexibility differ strongly across clients | Global models may underperform for minority users, unusual feeders, or rare events, weakening reliability and fairness | |
Privacy leakage through updates | Local data retention does not prevent leakage through gradients, participation patterns, or model outputs | Utilities may still face privacy risk even when raw smart-meter data are not centralized | |
Adversarial robustness | Poisoning, false-data injection, compromised clients, and cyber-physical manipulation can target federated pipelines | Privacy-preserving learning may fail if attackers corrupt model updates or exploit trusted aggregation assumptions | |
Computation at the edge | Smart meters and gateways may have limited processing, memory, and energy budgets | Cryptographic protection, split learning, and deep models may be difficult to maintain at scale | |
Benchmark inconsistency | Studies use different datasets, threat models, metrics, and privacy definitions | Claims of superiority are difficult to compare, reproduce, or translate into procurement and regulation |
Regulatory compliance remains underdeveloped in much of the technical literature, even when privacy is the stated motivation. Differential privacy for smart-metering provides formal protection concepts, but translating privacy budgets into regulatory acceptability is still unresolved [1]. Privacy preservation reviews highlight that smart-meter data governance involves consent, access control, retention, and secondary use, not only model-training architecture [27]. This review finds that federated learning studies often do not specify who controls aggregation, who audits privacy claims, or how consumers can exercise data rights.
Operational deployment faces barriers that are not captured by offline simulations or historical-data experiments. Federated split learning for smart meters points toward practical edge intelligence, yet maintaining models across heterogeneous meter generations, firmware environments, and communication links remains a difficult operational task [9]. Federated risk assessment shows the value of distributed learning for grid decision support, but real operators also need explainability, fallback procedures, and integration with supervisory control systems [18]. Few studies address maintenance responsibility, incident response, or liability when a privacy-preserving model contributes to an operational decision.
Scalability to millions of meters is not merely a larger version of laboratory-scale federation. Communication-efficient prediction schemes and asynchronous federated learning reduce some scaling pressure, but both still require careful orchestration of participation, update timing, and model-version control [12, 17]. Federated smart-meter data sharing raises additional scalability concerns because synthetic or shared representations may still require privacy validation across diverse customer populations [11]. Table 3 maps the main privacy and deployment gaps to regulatory, technical, and operational barriers, while proposing directions that are consistent with the evidence reviewed.
Table 3. Privacy and Deployment Gaps with Proposed Solutions
Gap | Deployment barrier | Barrier type | Proposed solution |
Undefined privacy budgets for energy data | Utilities and regulators lack shared thresholds for acceptable privacy-utility trade-offs | Regulatory | Develop sector-specific privacy-budget guidance for metering, forecasting, demand response, and DER coordination |
Weak governance of aggregation roles | Aggregators, utilities, vendors, and platform operators may have unclear responsibilities | Regulatory | Require auditable role definitions, data-processing agreements, and independent privacy audits |
Limited edge-device capability | Smart meters may not support heavy cryptography, deep models, or frequent updates | Technical | Use lightweight models, split learning, tiered aggregation, and hardware-aware privacy mechanisms |
Incomplete adversarial threat models | Studies often isolate poisoning, inference, or false-data attacks rather than combining them | Technical | Build unified threat models covering clients, aggregators, communication links, and cyber-physical impacts |
Poor integration with utility operations | Research prototypes rarely connect to control rooms, outage management, or DER management systems | Operational | Conduct field pilots with operator-in-the-loop evaluation, fallback procedures, and incident-response protocols |
Lack of comparable benchmarks | Claims are difficult to reproduce across datasets and privacy assumptions | Operational | Establish shared benchmark suites reporting accuracy, privacy leakage, communication cost, latency, robustness, and fairness |
Future research should prioritize communication-efficient federated architectures designed specifically for utility communication networks rather than generic distributed machine-learning environments. Asynchronous adaptive federated learning is a useful step because it reduces strict synchronization assumptions in distributed load forecasting [12]. Communication-efficient energy prediction also shows that model exchange can be optimized, but future designs need event-triggered updates, hierarchical aggregation, and feeder-aware participation policies [17]. This review finds that communication efficiency should be evaluated alongside operational latency, outage resilience, and cyber-security rather than as a standalone machine-learning metric.
Hybrid privacy models are likely to be more realistic than reliance on any single privacy-preserving technique. Federated learning can reduce raw-data centralization, differential privacy can bound individual contribution leakage, and cryptographic aggregation can protect updates during collaboration [14]. Differentially private optimal power flow and renewable-energy smart-metering studies show that formal privacy can be integrated into energy optimization, but utility loss must be explicitly managed [5, 6]. Future work should therefore develop layered privacy architectures with transparent privacy accounting, attack testing, and adaptive protection levels for analytics, control, and DER coordination.
The most important next step is movement from retrospective datasets to regulated field trials and sandboxes. Federated reinforcement learning for smart-home energy management and decentralized peer-to-peer energy trading show that privacy-preserving learning can support active coordination, but these approaches need validation under real tariffs, customer behavior, and operational constraints [8, 23]. Cyber-secure DER coordination research further indicates that privacy, auditability, and resilience must be tested together before deployment [24]. This review finds that interdisciplinary pilots involving utilities, regulators, vendors, aggregators, and consumers are essential to determine whether privacy-preserving learning can be trusted in real energy systems.
This critical review finds that federated and privacy-preserving learning has become a credible research direction for smart-meter analytics, grid-edge intelligence, and DER coordination. The strongest evidence appears in load forecasting, theft detection, privacy-aware prediction, and emerging edge-intelligence architectures. These studies show that decentralizing data access can reduce direct exposure of sensitive consumption records. However, the field is still far from proving full operational readiness.
The central weakness is that privacy is often framed too narrowly. Keeping raw data local is important, but it does not automatically prevent leakage through model updates, inferred labels, participation behavior, or compromised clients. Stronger privacy mechanisms can improve protection, yet they frequently introduce accuracy loss, computational cost, latency, and governance complexity. The practical question is therefore not whether privacy-preserving learning works in principle, but whether it works safely, reliably, and accountably in real grid environments.
The review also finds that DER coordination raises harder challenges than offline smart-meter analytics. EV charging, flexible demand, batteries, and peer-to-peer trading require timely decisions, fair incentives, cyber-resilience, and compatibility with distribution-network limits. These requirements expose gaps in benchmarking, threat modeling, regulatory interpretation, and operational integration. Future progress depends on evaluating privacy-preserving learning as part of the energy system, not only as a machine-learning protocol.
Federated and privacy-preserving learning should therefore be advanced through collaborative field pilots, standardized benchmarks, transparent privacy accounting, and sector-specific regulation. Utilities, researchers, technology providers, regulators, and consumer representatives need shared methods for testing privacy, accuracy, robustness, fairness, and control performance. Without this coordination, the field risks producing elegant algorithms that remain difficult to deploy. With it, privacy-preserving intelligence can become a foundation for trustworthy digital energy systems.
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