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cs.LG2024
Boosting the Performance of Decentralized Federated Learning via Catalyst Acceleration
Qinglun Li, Miao Zhang, Yingqi Liu +3
Decentralized Federated Learning has emerged as an alternative to centralized architectures due to its faster training, privacy preservation, and reduced communication overhead. In…
cs.LG2024
OledFL: Unleashing the Potential of Decentralized Federated Learning via Opposite Lookahead Enhancement
Qinglun Li, Miao Zhang, Mengzhu Wang +2
Decentralized Federated Learning (DFL) surpasses Centralized Federated Learning (CFL) in terms of faster training, privacy preservation, and light communication, making it a promis…
cs.LG2024
The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness
Mengyao Du, Miao Zhang, Yuwen Pu +3
To tackle the scarcity and privacy issues associated with domain-specific datasets, the integration of federated learning in conjunction with fine-tuning has emerged as a practical…