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Changlong Shi

2 accepted papers

2025

FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors

CVPR 2025poster

Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving privacy and enhancing security. However, data heterogeneity resulting from differences across user behaviors, preference…

2025

FedLWS: Federated Learning with Adaptive Layer-wise Weight Shrinking

ICLR 2025poster

In Federated Learning (FL), weighted aggregation of local models is conducted to generate a new global model, and the aggregation weights are typically normalized to 1. A recent study identifies the global weight shrinking effect in FL, indicating an enhancement in the global model’s generalization…