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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

Showing 1120 of 311 papers

TitleStatusHype
Confidence-aware Personalized Federated Learning via Variational Expectation MaximizationCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
DisPFL: Towards Communication-Efficient Personalized Federated Learning via Decentralized Sparse TrainingCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Efficient Split-Mix Federated Learning for On-Demand and In-Situ CustomizationCode1
Adaptive Test-Time Personalization for Federated LearningCode1
Exploiting Shared Representations for Personalized Federated LearningCode1
Bold but Cautious: Unlocking the Potential of Personalized Federated Learning through Cautiously Aggressive CollaborationCode1
An Empirical Study of Personalized Federated LearningCode1
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