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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 1–10 of 311 papers

TitleStatusHype
pFedMMA: Personalized Federated Fine-Tuning with Multi-Modal Adapter for Vision-Language ModelsCode0
Personalized Federated Learning via Dual-Prompt Optimization and Cross Fusion—0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
pFedSOP : Accelerating Training Of Personalized Federated Learning Using Second-Order Optimization—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
Federated Learning with Unlabeled Clients: Personalization Can Happen in Low Dimensions—0
Personalized Federated Learning under Model Dissimilarity Constraints—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
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