A domain-sensitive federated pruning framework that preserves domain-invariant structures while retaining domain-specific representations and a structure-aware aggregation algorithm that fuses heterogeneous personalized architectures into a domain-generalized global model is proposed.
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...
Mirko Konstantin, S. Zachow, Anirban Mukhopadhyay· 0 citations
This work introduces a novel multi-domain federated learning framework in which lightweight client-side proxy models collaborate with a server-side Foundation Model (FM) to learn new concepts without sharing private data.
Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh et al.· Trans. Mach. Learn. Res.· 0 citations
FedADB, a Class Anchor-Driven Dual-Branch FL framework, a dual-branch collaborative training mechanism designed for clients that achieves significant improvements in both accuracy and convergence speed.
Zhenyan Liu, Hua Zhang, Haoran Gao et al.· 0 citations
Federated learning (FL) is a practical framework that can train models on distributed user data while guaranteeing data privacy; however, due to heterogeneity in which each user has a different data distribution, problems frequently arise where both global and personalization performance deteriorate simultaneously. Thi...
A domain-aware proxy selection framework to better adopt proxy data for OOD problems is proposed and the experimental results show that the proposed models effectively address the challenges of distribution shifts under OOD with and without proxy data.
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.
Jia-Hao Wu, Ming Hu, Yanxin Yang et al.· Neural Information Processin...· 1 citation
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