A Causal-Driven Hierarchical Decentralised Federated Learning Framework for Resilient Load Forecasting in Distributed Microgrids

Inertia-Informed Federated Learning Control Framework for Distributed Smart Grid Resilience

Resilient-by-design smart grid control demands frameworks capable of maintaining stability under physical disturbances and communication failures, without reliance on centralized coordination. While Centralized Training Decentralized Execution (CTDE) enables a learning-based control paradigm at the grid edge, individually trained models fail to generalize across unseen fault contingencies and fall short of fully decentralized deployment. Federated learning (FL) restores generalization through collaborative training; however, standard aggregation strategies remain agnostic to the physical heterogeneity of synchronous generators. This work proposes Inertia-Informed Weighted FedAvg (IIWFedAvg), a physics-informed aggregation strategy that embeds generator inertia directly into global model fusion for transient stability control in transmission networks. The proposed framework further integrates interpretable Chebyshev Kolmogorov-Arnold Network (ChebyKAN)-based controllers, augmented with Rate-of-Change-of-Frequency (RoCoF) features to enhance dynamic response awareness. Evaluated on the IEEE 39-bus benchmark under full decentralized deployment, IIWFedAvg achieves a 75% generalization success rate across unseen fault contingencies. It also surpasses the centralized baseline in two out of three stabilized faults, while delivering a 3x improvement in stabilization speed at zero centralized coordination overhead.

Privacy-preserving load forecasting in smart grids using federated learning: a comparative analysis of aggregation strategies

Experimental results on real-world energy consumption datasets demonstrate that the proposed FL framework achieves competitive forecasting accuracy while preserving client data privacy, and a rigorous comparative analysis reveals that FedProx and FedTrimmedAvg consistently outperform FedAvg under non-IID conditions.

Entropy-Regularized Weighted Federated Meta-Learning Approach for Power Load Forecasting in Non-IID Scenarios

Formal Logic Inference Guided Uncertainty Quantification for Personalized Federated Learning

Federated Learning (FL) enables privacy-preserving model training across heterogeneous distributed systems, such as smartgrid forecasting or traffic-flow prediction from geographically dispersed sensors and devices. A key challenge in such settings is capturing client-specific patterns while addressing data heterogeneity and uncertainty at scale. Existing approaches, including Bayesian Neural Networks (BNNs) and clustering-based methods, struggle with scalability and consistent personalization. We propose LogiCP, a novel FL framework that integrates formal logic reasoning with uncertainty quantification (UQ) to support scalable and personalized learning with theoretical guarantees. LogiCP uses Signal Temporal Logic (STL) to extract temporal patterns and form semantically coherent client clusters, controlling intra-cluster heterogeneity. Within each cluster, LogiCP applies decentralized Conformal Prediction (CP) to produce distribution-free prediction intervals with mathematical guarantees that encompass the real value. LogiCP dynamically assigns clients to clusters at runtime without retraining, improving practicality. Evaluations on three real-world datasets—traffic, temperature, and electricity—show that LogiCP consistently outperforms BNN-, clustering-, and CP-based baselines, achieving up to a 95% improvement in client-level MSE while maintaining strong scalability.

Event-Triggered Decentralized Intelligence with Energy-Aware Federated Learning for Real-World Systems

The rapid growth of large-scale interconnected systems, such as smart cities, industrial automation, and environmental monitoring, demands intelligent decision-making frameworks that are resilient, scalable, and resource-efficient. Traditional centralized intelligence approaches suffer from communication bottlenecks, high energy demands, and vulnerability to single points of failure, making them unsuitable for realworld deployment. This work introduces an event-triggered decentralized intelligence framework with energy-aware federated learning designed to address these challenges. In the proposed system, distributed nodes collaborate by exchanging model updates only when significant events or anomalies occur, rather than relying on continuous communication. This event-driven strategy substantially reduces bandwidth consumption while enabling timely adaptation to dynamic environments. To further enhance sustainability, the framework integrates energy-aware scheduling, allowing devices with limited power resources to contribute adaptively based on their energy profiles. A multilayer coordination mechanism ensures local autonomy and global consensus without centralized control. Experimental evaluations on representative real-world datasets demonstrate that the proposed method achieves competitive accuracy compared to conventional federated learning while reducing communication overhead by more than 40% and extending device lifetime in energy-constrained settings. Additionally, the framework incorporates Byzantine-resilient aggregation and is analyzed under communication latency and varying network topology conditions.

Safe Multi-Agent Collaborative Learning for Networked Grid Operation Under Power Network Coupling Constraints

This paper formulate networked grid operation as a constrained decentralized partially observable Markov decision process and proposes a safe multi-agent collaborative learning framework that aims to reduce operating cost, load shedding, renewable curtailment, and carbon-relevant corrective burden.