# Communication Efficient Federated Learning for Generalized Linear Bandits

arXiv: https://arxiv.org/abs/2202.01087 · HTML: https://mlsanity.com/paper/2202.01087/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 2 Feb 2022.

## Builds on (most cited first, 16 of 16)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [FedSplit: An algorithmic framework for fast federated optimization](https://mlsanity.com/paper/2005.05238/) (2020)
- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Scalable Generalized Linear Bandits: Online Computation and Hashing](https://mlsanity.com/paper/1706.00136/) (2017)
- [Federated Multi-armed Bandits with Personalization](https://mlsanity.com/paper/2102.13101/) (2021)
- [Federated Linear Contextual Bandits](https://mlsanity.com/paper/2110.14177/) (2021)
- [Communication Complexity of Distributed Convex Learning and Optimization](https://mlsanity.com/paper/1506.01900/) (2015)
- [Distributed Clustering of Linear Bandits in Peer to Peer Networks](https://mlsanity.com/paper/1604.07706/) (2016)
- [A Gang of Bandits](https://mlsanity.com/paper/1306.0811/) (2013)
- [Asynchronous Upper Confidence Bound Algorithms for Federated Linear Bandits](https://mlsanity.com/paper/2110.01463/) (2021)
- [An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling](https://mlsanity.com/paper/2006.04012/) (2020)
- [Online Stochastic Linear Optimization under One-bit Feedback](https://mlsanity.com/paper/1509.07728/) (2015)

## Built on by (most cited first, 13 of 13)

- [A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits](https://mlsanity.com/paper/2207.03106/) (2022)
- [Federated Neural Bandits](https://mlsanity.com/paper/2205.14309/) (2022)
- [Federated Linear Contextual Bandits with User-level Differential Privacy](https://mlsanity.com/paper/2306.05275/) (2023)
- [Efficient Algorithms for Generalized Linear Bandits with Heavy-tailed Rewards](https://mlsanity.com/paper/2310.18701/) (2023)
- [Online Experimental Design With Estimation-Regret Trade-off Under Network Interference](https://mlsanity.com/paper/2412.03727/) (2024)
- [Byzantine-Robust Federated Linear Bandits](https://mlsanity.com/paper/2204.01155/) (2022)
- [Pure Exploration in Asynchronous Federated Bandits](https://mlsanity.com/paper/2310.11015/) (2023)
- [Incentivized Communication for Federated Bandits](https://mlsanity.com/paper/2309.11702/) (2023)
- [Federated Linear Bandits with Finite Adversarial Actions](https://mlsanity.com/paper/2311.00973/) (2023)
- [Incentivized Truthful Communication for Federated Bandits](https://mlsanity.com/paper/2402.04485/) (2024)
- [Federated Linear Dueling Bandits](https://mlsanity.com/paper/2502.01085/) (2025)
- [Communication-Efficient Federated Non-Linear Bandit Optimization](https://mlsanity.com/paper/2311.01695/) (2023)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)

## Abstract (arXiv)

Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the client side. But limited by the required communication efficiency, existing solutions are restricted to linear models to exploit their closed-form solutions for parameter estimation. Such a restricted model choice greatly hampers these algorithms' practical utility. In this paper, we take the first step to addressing this challenge by studying generalized linear bandit models under the federated learning setting. We propose a communication-efficient solution framework that employs online regression for local update and offline regression for global update. We rigorously proved, though the setting is more general and challenging, our algorithm can attain sub-linear rate in both regret and communication cost, which is also validated by our extensive empirical evaluations.