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This paper presents a projected stochastic gradient descent algorithm for Byzantine-robust and communication-efficient personalized federated learning (PFL), addressing the challenges of data heterogeneity and Byzantine worker attacks. The proposed method incorporates an adaptive threshold-selecting strategy for robust aggregation and employs random communication to enhance efficiency, allowing multiple local updates per communication round. Numerical experiments demonstrate the algorithm
- Author
- 8zb05lz7j
- Language
- EN