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Personalized Federated Learning

The federated learning setup presents numerous challenges including data heterogeneity (differences in data distribution), device heterogeneity (in terms of computation capabilities, network connection, etc.), and communication efficiency. Especially data heterogeneity makes it hard to learn a single shared global model that applies to all clients. To overcome these issues, Personalized Federated Learning (PFL) aims to personalize the global model for each client in the federation.

Papers

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Showing 131–140 of 311 papers

TitleStatusHype
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
FedASTA: Federated adaptive spatial-temporal attention for traffic flow prediction—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Contextual Stochastic Bilevel Optimization—0
An Optimal Transport Approach to Personalized Federated Learning—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
Influence-oriented Personalized Federated Learning—0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization—0
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