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A systematic survey of Federated Explainable Artificial Intelligence (FedXAI), covering roles, architectures, evaluation practices, and open challenges. It presents a multi-axis taxonomy and discusses model-agnostic to interpretable-by-design approaches, highlighting gaps in standardization and privacy-aware evaluation.
This paper introduces TallyTrain, a communication-efficient federated distillation method that transmits only the argmax class index per probe (hard-label consensus) instead of full softmax vectors, reducing bandwidth by up to three orders of magnitude while matching or surpassing the performance of soft-label distillation and Pareto-dominating standard federated learning baselines like FedAvg, FedProx, and FedDF.
This paper proposes FedFMX, a Fisher-Routed Mixture of Experts framework for Federated Class-Incremental Learning, addressing capacity conflict, catastrophic forgetting, and data heterogeneity via adaptive expert specialization.
This paper introduces BeyondArena, a unified holistic benchmark for tabular data, and finds that existing tabular foundation models excel only on small to medium-sized IID data, while traditional tree-based and deep learning models still dominate on non-IID, large, and high-dimensional datasets.
This paper introduces FedEPD, a framework for federated graph learning under long-tailed data distributions. It uses an energy-guided dual decoupling approach to separate topological purification from semantic recalibration, achieving state-of-the-art performance on benchmarks with up to 4.97% accuracy improvement.
Proposes Federated Nested Learning (FedNL), a framework that reformulates federated learning as a three-level nested optimization system, enabling collaborative training of self-referential memories for test-time adaptation to handle Non-IID data and long-tail distributions.