mlsanityData to 14 Oct 2026

Federated Linear Bandit Learning via Over-the-Air Computation

arXiv 2308.13298 ↗First posted 25 Aug 2023cs.LG eess.SP stat.MLcited by 0

Not on the map. Why?

Abstract (arXiv)

In this paper, we investigate federated contextual linear bandit learning within a wireless system that comprises a server and multiple devices. Each device interacts with the environment, selects an action based on the received reward, and sends model updates to the server. The primary objective is to minimize cumulative regret across all devices within a finite time horizon. To reduce the communication overhead, devices communicate with the server via over-the-air computation (AirComp) over noisy fading channels, where the channel noise may distort the signals. In this context, we propose a customized federated linear bandits scheme, where each device transmits an analog signal, and the server receives a superposition of these signals distorted by channel noise. A rigorous mathematical analysis is conducted to determine the regret bound of the proposed scheme. Both theoretical analysis and numerical experiments demonstrate the competitive performance of our proposed scheme in terms of regret bounds in various settings.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 28 references that are in the map. First posted on arXiv 25 Aug 2023.

Builds on

14 of 28 references.

References · 14

  1. 2018Federated Learning via Over-the-Air Computationcited by 219
  2. 2018Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)cited by 191
  3. 2019Federated Learning over Wireless Fading Channelscited by 183
  4. 2020One-Bit Over-the-Air Aggregation for Communication-Efficient Federated Edge Learning: Design and Convergence Analysiscited by 112
  5. 2020Differentially-Private Federated Linear Banditscited by 83
  6. 2018Differentially Private Contextual Linear Banditscited by 82
  7. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 62
  8. 2021Federated Multi-Armed Banditscited by 59
  9. 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
  10. 2023On Differentially Private Federated Linear Contextual Banditscited by 14
  11. 2020Bayesian Algorithms for Decentralized Stochastic Banditscited by 11
  12. 2022Over-the-Air Federated Learning via Second-Order Optimizationcited by 11
2 more
  1. 2022Distributed Bandits with Heterogeneous Agentscited by 9
  2. 2015Private Approximations of the 2nd-Moment Matrix Using Existing Techniques in Linear Regressioncited by 8

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2021Solving Multi-Arm Bandit Using a Few Bits of Communication
  2. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Air
  3. 2023Channel and Gradient-Importance Aware Device Scheduling for Over-the-Air Federated Learning
  4. 2024Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis
  5. 2025Over-the-Air Federated Learning: Rethinking Edge AI Through Signal Processing
  6. 2026Batched Stochastic Linear Bandits with 1-Bit Communication Constraints
  7. 2026Offline-to-Online Learning in Linear Bandits
  8. 2025Adaptive Client Selection via Q-Learning-based Whittle Index in Wireless Federated Learning