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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 51–75 of 311 papers

TitleStatusHype
One Model to Unite Them All: Personalized Federated Learning of Multi-Contrast MRI SynthesisCode1
Dual‑detector Re‑optimization for Federated Weakly Supervised Video Anomaly Detection Via Adaptive Dynamic Recursive MappingCode1
On Bridging Generic and Personalized Federated Learning for Image ClassificationCode1
Ditto: Fair and Robust Federated Learning Through PersonalizationCode1
Characterizing Internal Evasion Attacks in Federated LearningCode1
FedAS: Bridging Inconsistency in Personalized Federated LearningCode1
Parameterized Knowledge Transfer for Personalized Federated LearningCode1
Personalized Federated Learning by Structured and Unstructured Pruning under Data HeterogeneityCode1
Personalization Improves Privacy-Accuracy Tradeoffs in Federated LearningCode0
BTFL: A Bayesian-based Test-Time Generalization Method for Internal and External Data Distributions in Federated learningCode0
Aggregating Intrinsic Information to Enhance BCI Performance through Federated LearningCode0
Personalized Federated Learning: A Unified Framework and Universal Optimization TechniquesCode0
Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated LearningCode0
Blinder: End-to-end Privacy Protection in Sensing Systems via Personalized Federated LearningCode0
PeFLL: Personalized Federated Learning by Learning to LearnCode0
Motley: Benchmarking Heterogeneity and Personalization in Federated LearningCode0
DualFed: Enjoying both Generalization and Personalization in Federated Learning via Hierachical RepresentationsCode0
Low-Resource Machine Translation through the Lens of Personalized Federated LearningCode0
Memory-Based Optimization Methods for Model-Agnostic Meta-Learning and Personalized Federated LearningCode0
Learn What You Need in Personalized Federated LearningCode0
Adaptive Expert Models for Personalization in Federated LearningCode0
Loop Improvement: An Efficient Approach for Extracting Shared Features from Heterogeneous Data without Central ServerCode0
Personalized Federated Learning on Long-Tailed Data via Adversarial Feature AugmentationCode0
FHBench: Towards Efficient and Personalized Federated Learning for Multimodal HealthcareCode0
Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser SchemeCode0
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