Research map / Bandits and online learning / Contextual bandits and Thompson sampling
Federated Linear Contextual Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use all historical observations (Enhanced Fed-PE) instead of only the previous phase | Fed-PE · Synthetic, M=100, K=10, d=3 | per-client cumulative regret | Helped |
| keep feature vectors private (Fed-PE) versus full information exchange (Collaborative) | Fed-PE · Synthetic, M=100, K=10, d=3 | per-client cumulative regret | No clear effect |
| add communication among clients (Fed-PE family vs local UCB) | Local UCB · Synthetic and MovieLens-100K | per-client cumulative regret | Helped |
| increase number of clients M | Enhanced Fed-PE · Synthetic, K=10, d=4 | per-client regret | Helped |
Closest papers by meaning
- Federated Linear Contextual Bandits with User-level Differential Privacy (ICML 2023 · similarity 0.91)
- Communication Efficient Federated Learning for Generalized Linear Bandits (NeurIPS 2022 · similarity 0.91)
- Federated X-armed Bandit (AAAI 2024 · similarity 0.89)
- Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users (AAAI 2024 · similarity 0.89)
- Communication-Efficient Federated Non-Linear Bandit Optimization (ICLR 2024 · similarity 0.89)
- Generalized Linear Bandits with Limited Adaptivity (NeurIPS 2024 · similarity 0.88)
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