Research map / Federated learning and privacy / Communication-efficient FL
Agnostic Federated Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize worst-case loss over mixtures of doctorate and non-doctorate domains instead of training on the uniform sample mixture | logistic regression · Adult, D_Λ | accuracy: 69.61± 0.35 -> 71.53 ± 0.88 | Helped |
| Optimize worst-case loss over clothing-class domains instead of training on the uniform sample mixture | logistic regression · Fashion MNIST, D_Λ | accuracy: 71.2 ± 7.8 -> 74.5 ± 6.0 | Helped |
| Optimize worst-case loss over document and conversation domains instead of training on the uniform sample mixture | two-layer LSTM · Language models, D_Λ | perplexity: 96.98 -> 86.33 | Helped |
Closest papers by meaning
- Adaptive Federated Learning with Auto-Tuned Clients (ICLR 2024 · similarity 0.88)
- Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability (NeurIPS 2024 · similarity 0.88)
- Decentralized Sporadic Federated Learning: A Unified Algorithmic Framework with Convergence Guarantees (ICLR 2025 · similarity 0.87)
- Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity (CVPR 2024 · similarity 0.87)
- Exact and Linear Convergence for Federated Learning under Arbitrary Client Participation is Attainable (NeurIPS 2025 · similarity 0.87)
- Pursuing Overall Welfare in Federated Learning through Sequential Decision Making (ICML 2024 · similarity 0.87)
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