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Distribution networks increasingly host distributed energy resources whose flexibility, if properly harnessed, can support grid operation. Smart-meter data provides a basis for estimating this flexibility by revealing consumption, generation, and behind-the-meter behavioural patterns. The lack of an integrated framework linking smart-meter analytics, DER availability, and operational decision-making hinders the realization of flexibility-aware smart grids. Existing approaches often treat data analytics, flexibility modelling, and distribution-level operation as separate domains. This article develops a conceptual framework that links smart-meter analytics, DER availability estimation, flexibility estimation, and decision support into a coherent system for distribution-level operation. The aim is to clarify how information can flow from metering data to actionable DSO decisions. A conceptual synthesis of literature on smart-meter analytics, DER modelling, flexibility estimation, aggregation, and DSO decision-support architectures is performed. No empirical data, simulations, experiments, or quantitative analyses are used. The framework comprises three interrelated layers: smart-meter analytics, DER availability estimation, and decision support, connected through a flexibility estimation layer. Propositions regarding data granularity, flexibility accuracy, uncertainty handling, and operational benefit are articulated. Two tables summarize DER availability indicators and framework architecture. The framework provides a foundation for future empirical testing and can guide DSOs in building flexibility-aware operational capabilities. It also clarifies how data, flexibility, and decision processes should be conceptualized as linked components of smart-grid operation.

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.