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FeLiX is a new federated learning orchestration framework that optimizes time-to-target accuracy on live interaction streams by handling transient client availability, dynamic data heterogeneity, and outcome delays. It introduces streaming-aware availability tiers, fresh-utility selection, and delay-robust aggregation, reducing wall-clock time by up to 2.37x and communication bandwidth by 1.30x versus state-of-the-art baselines.
This paper introduces COSMOS, a model-agnostic personalized federated learning framework that uses clustered server models and pseudo-label-only communication. It provides theoretical analysis showing exponential personalization risk contraction and demonstrates superior performance over existing baselines in heterogeneous environments.
This paper introduces FedeKD, a reliability-aware framework for federated knowledge distillation that uses an energy-based gating mechanism to mitigate negative transfer in heterogeneous settings. The authors demonstrate that weighting knowledge transfer based on sample-wise trust improves robustness and predictive performance without requiring public datasets.
The article explains the concept of Federated Learning as a privacy-preserving machine learning technique that trains models on local devices rather than central servers. It details the process of encrypted parameter updates and aggregation to mitigate data leakage risks while maintaining model performance.