Research map / Federated learning and privacy / Differential privacy
Differentially-Private Federated Linear Bandits
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| synchronize every round (O(MT) communication) instead of O(M log T) communication (ablation) | Centralized FedUCB · regret bound | group pseudoregret | Helped |
| use private noise (epsilon=1) vs non-private across communication levels (ablation) | Centralized FedUCB · synthetic linear bandit, M=100 | regret | Hurt |
Closest papers by meaning
- On Differentially Private Federated Linear Contextual Bandits (ICLR 2024 · similarity 0.93)
- Federated Linear Contextual Bandits with User-level Differential Privacy (ICML 2023 · similarity 0.92)
- Communication-Efficient Federated Non-Linear Bandit Optimization (ICLR 2024 · similarity 0.90)
- Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users (AAAI 2024 · similarity 0.90)
- Federated X-armed Bandit (AAAI 2024 · similarity 0.88)
- Shuffle and Joint Differential Privacy for Generalized Linear Contextual Bandits (NeurIPS 2026 · similarity 0.88)
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