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cs.DC2025
FLAMMABLE: A Multi-Model Federated Learning Framework with Multi-Model Engagement and Adaptive Batch Sizes
Shouxu Lin, Zimeng Pan, Yuhang Yao +3
Multi-Model Federated Learning (MMFL) is an emerging direction in Federated Learning (FL) where multiple models are trained in parallel, generally on various datasets. Optimizing t…
cs.CR2025
Evaluating Selective Encryption Against Gradient Inversion Attacks
Jiajun Gu, Yuhang Yao, Shuaiqi Wang +1
Gradient inversion attacks pose significant privacy threats to distributed training frameworks such as federated learning, enabling malicious parties to reconstruct sensitive local…