Research map / Federated learning and privacy / Communication-efficient FL
Personalized Federated Learning with Moreau Envelopes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| pFedMe personalized model vs Per-FedAvg | MLR · Synthetic | accuracy: 81.49 -> 83.20 | Helped |
| pFedMe personalized model vs FedAvg | DNN · MNIST | accuracy: 98.79 -> 99.46 | Helped |
| use personalized model instead of pFedMe global model (ablation) | pFedMe · Synthetic (MLR) | accuracy: 78.65 -> 83.20 | Helped |
| pFedMe global model vs Per-FedAvg | MLR · MNIST | accuracy: 94.37 -> 94.18 | Hurt |
| increase local personalization steps K beyond 5 (e.g., 7) (ablation) | pFedMe · MNIST | convergence | No clear effect |
| increase global update parameter beta above 1 (ablation) | pFedMe · MNIST | convergence speed | Helped |
Closest papers by meaning
- Decentralized Directed Collaboration for Personalized Federated Learning (CVPR 2024 · similarity 0.90)
- pFedES: Generalized Proxy Feature Extractor Sharing for Model Heterogeneous Personalized Federated Learning (AAAI 2025 · similarity 0.89)
- pFedClub: Controllable Heterogeneous Model Aggregation for Personalized Federated Learning (NeurIPS 2024 · similarity 0.89)
- FedLMT: Tackling System Heterogeneity of Federated Learning via Low-Rank Model Training with Theoretical Guarantees (ICML 2024 · similarity 0.89)
- Understanding the Statistical Accuracy-Communication Trade-off in Personalized Federated Learning with Minimax Guarantees (ICML 2025 · similarity 0.89)
- PeFLL: Personalized Federated Learning by Learning to Learn (ICLR 2024 · similarity 0.89)
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