TL;DR
This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation.
This paper proposes federated clustering with adaptive personalization (FedCAP), a parameter-efficient personalized FL framework that separates cluster-level representation learning from client-level adaptation.
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...
A personalized federated sparse adaptation framework with a heterogeneous temporal mixture-of-experts (MoE) adapter placed after the pretrained TSFM representation, showing that federated TSFM adaptation should be both client-aware and backbone-aware.
: Personalized federated learning (PFL) aims to address the insufficient adaptability of a single global model caused by Non-IID data. It improves client-specific performance by learning customized models for different clients. However, existing PFL methods based on similarity modeling often struggle to balance persona...
Decentralized Machine Learning (ML) through Federated Learning (FL) offers an alternative to centralized machine learning while preserving privacy for handover prediction, but client data distributions in networks such as fifth-generation (5G) are non-Independent and Identically Distributed (non-IID), which influences...
This work proposes FedHAttn, a novel hierarchical attention–based aggregation mechanism that explicitly models inter-client model feature importance to optimize global model performance and establishes an effective aggregator that balances accuracy, robustness, and efficiency in federated PM2.5 prediction.
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