Research map / Federated learning and privacy / Communication-efficient FL
Federated Bayesian Optimization via Thompson Sampling
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FTS instead of standard Thompson sampling (TS) | Bayesian optimization with similar agents (d_n=0.02) · Synthetic functions | simple regret | Helped |
| Use FTS instead of RGPE or TAF | Federated Bayesian optimization · Landmine detection | AUC | Helped |
| Use FTS instead of RGPE or TAF | Federated Bayesian optimization · Google Glasses activity recognition | validation error | Helped |
| Use FTS instead of RGPE or TAF | Federated Bayesian optimization · Mobile phone activity recognition | validation error | Helped |
| Use FTS rather than RGPE or TAF | Federated Bayesian optimization · Real-world experiments | runtime | Helped |
| Increase the number of random Fourier features M (ablation) | FTS when agents have few observations · Synthetic functions, small t_n | simple regret | No clear effect |
| Increase the number of random Fourier features M (ablation) | FTS when agents have many observations · Synthetic functions, large t_n | simple regret | Helped |
| Increase agent observations from t_n=40 to t_n=100 (ablation) | FTS with similar agent objectives and M=100 · Synthetic functions | simple regret: t_n=40 vs. t_n=100 | Helped |
| Use information from heterogeneous agents with d_n=1.2 (ablation) | FTS · Synthetic functions | simple regret | No clear effect |
| Make p_t grow faster so other agents' influence decays faster (ablation) | FTS with heterogeneous agents · Synthetic functions, d_n=1.2 | simple regret | Helped |
| Increase the number of agents (ablation) | FTS · Landmine detection | runtime | No clear effect |
| Increase the number of agents (ablation) | RGPE and TAF · Landmine detection | runtime | Hurt |
Closest papers by meaning
- FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling (NeurIPS 2025 · similarity 0.87)
- Communication-Efficient Federated Non-Linear Bandit Optimization (ICLR 2024 · similarity 0.86)
- A Bayesian Approach for Personalized Federated Learning in Heterogeneous Settings (NeurIPS 2024 · similarity 0.85)
- FedRSPO+: A Heterogeneity-aware Algorithm for Decision-focused Federated Learning (NeurIPS 2026 · similarity 0.85)
- A Study of Bayesian Neural Network Surrogates for Bayesian Optimization (ICLR 2024 · similarity 0.84)
- Federated Optimization with Doubly Regularized Drift Correction (ICML 2024 · similarity 0.84)
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