TL;DR
FedALA-WR, a novel framework that integrates adaptive attention-based aggregation with a weighted memory replay mechanism, allowing clients to reinforce valuable past knowledge during local training, consistently outperforms baseline FL and state-of-the-art pFL methods in accuracy and convergence speed.
Local Inference Guided Aggregation for Heterogeneous Training Environments to Yield Enhancement Through Agreement and Regularization (LIGHTYEAR), a federated learning framework that performs update selection in function space using an NTK-based agreement score to characterize predictive behavior and determine a persona...
FedRAM is proposed, a three-step framework that progressively updates two scalar hyperparameters: the task importance weight and the client aggregation coefficient, where the proxy model serves as an intermediate between the local reference model and the global agent model.
Personalized Federated Learning (pFL) has emerged as a promising paradigm, while existing approaches face 3 limitations: granularity mismatch, resource waste, and conflict aggregation. This paper presents a Personalized Federated Learning with Low-rank Pruning-based Adaptation (pFedLoPA) framework. Instead of balancing...
A novel FL framework is presented, FedPhoenix, that stochastically re-sets partial parameters in each round to destroy some features of the global model, guiding FL training to learn multiple generalized features for inference rather than specific overfitting features.
Federated Learning often suffers from significant performance degradation under Non-IID (non-identically and independently distributed) data settings, where heterogeneous client data distributions lead to severe model drift. Traditional aggregation strategies, such as FedAvg, typically determine client contribution wei...
Results indicate that multi-factor reliability assessment and heterogeneity-aware adaptive forgetting improve the balance between useful knowledge retention and harmful-knowledge suppression in heterogeneous FCL.
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