SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 61–70 of 311 papers

TitleStatusHype
Agnostic Personalized Federated Learning with Kernel Factorization—0
Enhancing Convergence, Privacy and Fairness for Wireless Personalized Federated Learning: Quantization-Assisted Min-Max Fair Scheduling—0
Energy-Aware Edge Association for Cluster-based Personalized Federated Learning—0
Electrical Load Forecasting over Multihop Smart Metering Networks with Federated Learning—0
Electrical Load Forecasting in Smart Grid: A Personalized Federated Learning Approach—0
FedCALM: Conflict-aware Layer-wise Mitigation for Selective Aggregation in Deeper Personalized Federated Learning—0
FedCRL: Personalized Federated Learning with Contrastive Shared Representations for Label Heterogeneity in Non-IID Data—0
Efficient Cluster Selection for Personalized Federated Learning: A Multi-Armed Bandit Approach—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Show:102550
← PrevPage 7 of 32Next →

No leaderboard results yet.