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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.