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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 126–150 of 311 papers

TitleStatusHype
Generalized and Personalized Federated Learning with Foundation Models via Orthogonal Transformations—0
On Heterogeneously Distributed Data, Sparsity Matters—0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss—0
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
Mobilizing Personalized Federated Learning in Infrastructure-Less and Heterogeneous Environments via Random Walk Stochastic ADMM—0
MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis—0
FedAPA: Server-side Gradient-Based Adaptive Personalized Aggregation for Federated Learning on Heterogeneous Data—0
Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learning—0
An Optimal Transport Approach to Personalized Federated Learning—0
Contextual Stochastic Bilevel Optimization—0
MH-pFLGB: Model Heterogeneous personalized Federated Learning via Global Bypass for Medical Image Analysis—0
Mitigating Membership Inference Vulnerability in Personalized Federated Learning—0
How to Backdoor HyperNetwork in Personalized Federated Learning?—0
Lazy But Effective: Collaborative Personalized Federated Learning with Heterogeneous Data—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning—0
Integrating Personalized Federated Learning with Control Systems for Enhanced Performance—0
Lower Bounds and Optimal Algorithms for Personalized Federated Learning—0
Influence-oriented Personalized Federated Learning—0
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning—0
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization—0
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
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