Research map / Bandits and online learning / Multi-armed bandits
Doubly Adversarial Federated Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use FedExp3 (federated Exp3 with gossip averaging of loss estimators) instead of GossipUCB | federated multi-armed bandit on MovieLens genre recommendation · MovieLens-Latest-full, complete graph, grid, RGG(0.5) | average cumulative regret: significant margin (no numbers in text) | Helped |
| Replace stationary-setting GossipUCB with the adversarial FedExp3 under non-stationary preferences | federated bandit on MovieLens · MovieLens non-stationary environment | cumulative regret: significant margin (no numbers in text) | Helped |
| Gossip matrix spectral gap: regret scales with (1-sigma_2(W))^{-1/3} as network connectivity changes (ablation) | FedExp3 on synthetic Bernoulli federated bandit (N=36, K=20, T=3000) · synthetic, RGG(d) d in {0.3..0.9}, grid, complete graph | cumulative regret | No clear effect |
| Sublinear regret growth with time for FedExp3 under doubly adversarial losses | FedExp3 · synthetic federated bandit | cumulative regret vs time | Helped |
Closest papers by meaning
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- Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users (AAAI 2024 · similarity 0.88)
- Stochastic Bandits Robust to Adversarial Attacks (ICLR 2025 · similarity 0.87)
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