# Learning from Unreliable Trajectories: Adversarially-Robust Federated Q-Learning

arXiv: https://arxiv.org/abs/2610.06918 · HTML: https://mlsanity.com/paper/2610.06918/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods.
- Builds on 34 of its 56 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Placed in

- Deep reinforcement learning methods / [Adversarial robustness and RL in finance](https://mlsanity.com/deeprl/adversarial-robustness-and-rl-in-finance/)

## Builds on (most cited first, 30 of 34)

- [Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates](https://mlsanity.com/paper/1803.01498/) (2018)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [A Finite Time Analysis of Temporal Difference Learning With Linear Function Approximation](https://mlsanity.com/paper/1806.02450/) (2018)
- [Finite-Time Analysis of Asynchronous Stochastic Approximation and Q-Learning](https://mlsanity.com/paper/2002.00260/) (2020)
- [Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis](https://mlsanity.com/paper/2102.06548/) (2021)
- [Federated Reinforcement Learning: Techniques, Applications, and Open Challenges](https://mlsanity.com/paper/2108.11887/) (2021)
- [Stochastic approximation with cone-contractive operators: Sharp ℓ_∞-bounds for Q-learning](https://mlsanity.com/paper/1905.06265/) (2019)
- [DRACO: Byzantine-resilient Distributed Training via Redundant Gradients](https://mlsanity.com/paper/1803.09877/) (2018)
- [Federated Reinforcement Learning with Environment Heterogeneity](https://mlsanity.com/paper/2204.02634/) (2022)
- [Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee](https://mlsanity.com/paper/2110.14074/) (2021)
- [Adapting to Mixing Time in Stochastic Optimization with Markovian Data](https://mlsanity.com/paper/2202.04428/) (2022)
- [The Blessing of Heterogeneity in Federated Q-Learning: Linear Speedup and Beyond](https://mlsanity.com/paper/2305.10697/) (2023)
- [Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning](https://mlsanity.com/paper/2401.15273/) (2024)
- [Federated Temporal Difference Learning with Linear Function Approximation under Environmental Heterogeneity](https://mlsanity.com/paper/2302.02212/) (2023)
- [Towards Understanding Asynchronous Advantage Actor-critic: Convergence and Linear Speedup](https://mlsanity.com/paper/2012.15511/) (2020)
- [On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence](https://mlsanity.com/paper/1411.3224/) (2014)
- [Improved Communication Efficiency in Federated Natural Policy Gradient via ADMM-based Gradient Updates](https://mlsanity.com/paper/2310.19807/) (2023)
- [Distributed Optimization Under Adversarial Nodes](https://mlsanity.com/paper/1606.08939/) (2016)
- [Federated TD Learning over Finite-Rate Erasure Channels: Linear Speedup under Markovian Sampling](https://mlsanity.com/paper/2305.08104/) (2023)
- [Distributed TD(0) with Almost No Communication](https://mlsanity.com/paper/2305.16246/) (2023)
- [Private and Byzantine-Proof Cooperative Decision-Making](https://mlsanity.com/paper/2205.14174/) (2022)
- [Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost](https://mlsanity.com/paper/2405.18795/) (2024)
- [The Sample-Communication Complexity Trade-off in Federated Q-Learning](https://mlsanity.com/paper/2408.16981/) (2024)
- [Linear Stochastic Approximation: Constant Step-Size and Iterate Averaging](https://mlsanity.com/paper/1709.04073/) (2017)
- [Byzantine-Robust Federated Linear Bandits](https://mlsanity.com/paper/2204.01155/) (2022)
- [Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments](https://mlsanity.com/paper/2405.19499/) (2024)
- [SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning](https://mlsanity.com/paper/2402.04114/) (2024)
- [Compressed Federated Reinforcement Learning with a Generative Model](https://mlsanity.com/paper/2404.10635/) (2024)
- [Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agents](https://mlsanity.com/paper/2410.22908/) (2024)

## Abstract (arXiv)

We study federated reinforcement learning in which multiple agents interact with a common Markov decision process and communicate through a central server to collaboratively learn the optimal state-action value function. Our goal is to understand whether the sample-efficiency benefits of collaboration can be retained when a fraction of the agents behave adversarially and transmit arbitrarily corrupted information. To address this problem, we introduce Robust Async-Fed-Q, an epoch-based federated learning algorithm that combines variance-reduced estimation of the Bellman optimality operator at the agents with robust aggregation at the server. We establish high-probability finite-time guarantees showing that the proposed method preserves the statistical gains of collaboration among the honest agents while tolerating adversarial corruption. In particular, the effect of the adversarial agents decreases as the amount of data collected by each honest agent grows and eventually vanishes in the infinite-sample limit. We complement these guarantees with information-theoretic lower bounds that characterize the unavoidable statistical cost of adversarial corruption, leading to the first nearly matching upper and lower bounds for adversarially robust federated reinforcement learning. We further extend our framework to accommodate single-trajectory Markovian sampling and heterogeneous partial coverage, where different agents may explore different regions of the state-action space and learning relies on their collective coverage. Finally, our epoch-based design substantially improves the best known communication complexity for federated Q-learning under asynchronous sampling.