Personalized Federated Learning
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Benchmarks
CIFAR-10
CIFAR-100
FEMNIST
MNIST
Omniglot
Shakespeare
Tiny ImageNet
Most implemented
Adaptive Personalized Federated Learning
PFLlib: A Beginner-Friendly and Comprehensive Personalized Federated Learning Library and Benchmark
Federated Multi-Task Learning under a Mixture of Distributions
GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning
Papers
Personalized Federated Learning through Global Knowledge Distillation and Local Head Adaptation
Statistical heterogeneity limits federated learning when a single global classifier cannot represent client-specific label distributions. In this work, we propose Personalized Federated Knowledge Distillation with Head A…
Personalized Federated LearningKnowledge DistillationCommunication-Efficient Personalized Federated Learning via Layer-Wise Multi-Threshold Random Sketching
Personalized federated learning (PFL) is a promising paradigm for collaborative learning over distributed devices, where edge nodes collaboratively train personalized models without sharing raw data. Although PFL address…
Personalized Federated LearningFlatLand: Personalized Graph Federated Learning via Tailored Lorentz Space
Federated learning enables privacy-preserving collaborative training, but highly heterogeneous client data remain challenging, especially in graph federated learning where clients possess structurally diverse graphs. Exi…
Personalized Federated LearningGraph LearningDual Attention Heads for Personalized Federated Learning in ECG Classification
Federated learning (FL) enables collaborative model training across institutions without sharing sensitive patient data. However, the inherent heterogeneity of electrocardiogram (ECG) data across healthcare providers pre…
Personalized Federated LearningECG ClassificationQFedAgent: Quantum-Enhanced Personalized Federated Learning for Multi-Agent Activity Recognition
Federated learning (FL) enables collaborative model training across distributed devices without sharing raw data, making it suitable for privacy-sensitive robotic sensing applications. However, multi-agent systems genera…
Personalized Federated LearningActivity RecognitionTowards Value-Constrained Credit Assignment in Fully Delegated AI Cooperatives
We propose a framework for reward allocation in fully delegated AI cooperatives where humans are represented by agents that contribute data and participate in model updates under heterogeneous value constraints. The key …
Personalized Federated Learning