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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 201–225 of 311 papers

TitleStatusHype
Addressing Data Heterogeneity in Federated Learning with Adaptive Normalization-Free Feature Recalibration—0
Advances and Challenges in Meta-Learning: A Technical Review—0
Advancing Personalized Federated Learning: Group Privacy, Fairness, and Beyond—0
Advancing Personalized Federated Learning: Integrative Approaches with AI for Enhanced Privacy and Customization—0
Agnostic Personalized Federated Learning with Kernel Factorization—0
An Optimal Transport Approach to Personalized Federated Learning—0
A Novel Algorithm for Personalized Federated Learning: Knowledge Distillation with Weighted Combination Loss—0
A novel parameter decoupling approach of personalized federated learning for image analysis—0
A Parameter Aggregation Strategy on Personalized Federated Learning—0
A Personalized Federated Learning Algorithm: an Application in Anomaly Detection—0
Asynchronous Personalized Federated Learning through Global Memorization—0
A Theorem of the Alternative for Personalized Federated Learning—0
Bad-PFL: Exploring Backdoor Attacks against Personalized Federated Learning—0
Bayesian Neural Network For Personalized Federated Learning Parameter Selection—0
Bayesian Personalized Federated Learning with Shared and Personalized Uncertainty Representations—0
CDKT-FL: Cross-Device Knowledge Transfer using Proxy Dataset in Federated Learning—0
Client-supervised Federated Learning: Towards One-model-for-all Personalization—0
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks—0
Contextual Stochastic Bilevel Optimization—0
Convergence of First-Order Algorithms for Meta-Learning with Moreau Envelopes—0
Convergence-Privacy-Fairness Trade-Off in Personalized Federated Learning—0
Cross-Fusion Rule for Personalized Federated Learning—0
DA-PFL: Dynamic Affinity Aggregation for Personalized Federated Learning—0
Decentralized Directed Collaboration for Personalized Federated Learning—0
Decentralized Personalized Federated Learning—0
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