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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 load forecasting is essential for distribution-grid operation, flexibility scheduling, congestion management, and renewable integration. However, centralizing household-level smart-meter records can expose private behavioral patterns and reduce consumer trust. This article proposes a federated forecasting framework for accurate smart-meter load prediction across heterogeneous distribution feeders. The framework enables multiple feeders to collaboratively train forecasting models without transferring raw household consumption data. The proposed framework combines feeder-local temporal neural networks, federated aggregation, feeder-aware personalization, and optional differential privacy. It is validated through simulation on multi-feeder smart-meter datasets with different customer mixes, photovoltaic penetration levels, missing-data rates, and load variability. The framework achieves forecasting accuracy close to a centralized privacy-free benchmark while outperforming local-only and non-personalized federated baselines under strong feeder heterogeneity. Differential privacy introduces a controlled accuracy penalty that remains acceptable for day-ahead and intra-day feeder operation. The proposed federated forecasting framework provides a practical privacy-preserving solution for distribution system operators that need accurate load forecasts across diverse feeders. The results indicate that collaborative learning can preserve household privacy while improving feeder-level prediction accuracy.

Electric vehicle charging imposes new operational stress on distribution grids, especially where local transformer capacity and feeder voltage margins are limited. Grid-edge intelligence could enable charging decisions to be made closer to the physical constraints that shape those impacts. Existing EV charging control architectures often depend on centralized optimization, cloud-side visibility, or stable communication paths. Such designs can create privacy, latency, and resilience gaps when local operating conditions change rapidly. This article proposes a layered grid-edge intelligence architecture for distributed EV charging control. The architecture integrates local forecasting, federated model updates, and resilience-aware coordination into a unified system design. The architecture is developed conceptually through functional layers, interfaces, data flows, and operational workflows. Implementation scenarios and validation stages are outlined to guide future simulation, hardware-in-the-loop testing, and field deployment. The proposed architecture defines local forecasting nodes, a federated update layer, and resilience coordination functions that interact through structured protocols. Five tables are specified across the full manuscript to summarize model roles, coordination strategies, resilience mechanisms, deployment scenarios, and validation procedures. The architecture offers a blueprint for grid-edge EV charging management that balances intelligence, privacy, and resilience. It is intended to support future practical implementation rather than report experimental results.

Accurate short-term forecasting of household electricity consumption is vital to contemporary distributed energy networks. Most approaches to load prediction rely on deep neural architectures and require access to confidential, user-specific consumption records during training. Traditional neural network training typically collects all available data at a single central location to develop one unified model. Nevertheless, pooling individual user information in this manner raises serious concerns regarding data security and personal privacy. To address these issues, the present research explores federated learning techniques combined with model personalization as promising alternatives. Through a comprehensive simulation-based evaluation, these approaches are benchmarked against the standard centralized training strategy and a simple pre-trained reference model. The analysis reveals that the intrinsic characteristics and heterogeneity of the underlying electricity consumption patterns significantly affect the resulting model error levels. Consequently, directly contrasting error distributions across methods largely reflects differences in data composition rather than true differences in algorithmic effectiveness. To overcome this limitation, the study introduces a novel evaluation technique termed “differential comparison.” This approach focuses exclusively on pairwise loss differences across identical data instances, thereby enabling a fairer assessment of relative model improvements via the resulting difference distributions. Applying this method demonstrates that personalized model training outperforms all other strategies examined for residential load prediction, achieving better results in 59.1% of the evaluated scenarios.