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SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning
[article]
2022
arXiv
pre-print
Federated learning (FL) facilitates the training and deploying AI models on edge devices. Preserving user data privacy in FL introduces several challenges, including expensive communication costs, limited resources, and data heterogeneity. In this paper, we propose SPATL, an FL method that addresses these issues by: (a) introducing a salient parameter selection agent and communicating selected parameters only; (b) splitting a model into a shared encoder and a local predictor, and transferring
arXiv:2111.14345v2
fatcat:wogvgqhkhjbj3k644sxv4zhife