# FedAA: A Reinforcement Learning Perspective on Adaptive Aggregation for Fair and Robust Federated Learning

arXiv: https://arxiv.org/abs/2402.05541 · HTML: https://mlsanity.com/paper/2402.05541/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 34 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

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

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [Local Model Poisoning Attacks to Byzantine-Robust Federated Learning](https://mlsanity.com/paper/1911.11815/) (2019)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping](https://mlsanity.com/paper/2012.13995/) (2020)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Survey of Personalization Techniques for Federated Learning](https://mlsanity.com/paper/2003.08673/) (2020)
- [Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation](https://mlsanity.com/paper/1903.03936/) (2019)
- [FairFed: Enabling Group Fairness in Federated Learning](https://mlsanity.com/paper/2110.00857/) (2021)
- [Generalized Byzantine-tolerant SGD](https://mlsanity.com/paper/1802.10116/) (2018)
- [Data Poisoning Attacks on Factorization-Based Collaborative Filtering](https://mlsanity.com/paper/1608.08182/) (2016)
- [Technical Report: When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks](https://mlsanity.com/paper/1803.06975/) (2018)
- [Federated Deep Reinforcement Learning](https://mlsanity.com/paper/1901.08277/) (2019)
- [Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning](https://mlsanity.com/paper/2304.12961/) (2023)

## Built on by (most cited first, 6 of 6)

- [Theoretical Analysis of Privacy Leakage in Trustworthy Federated Learning: A Perspective from Linear Algebra and Optimization Theory](https://mlsanity.com/paper/2407.16735/) (2024)
- [FedPF: Accurate Target Privacy Preserving Federated Learning Balancing Fairness and Utility](https://mlsanity.com/paper/2510.26841/) (2025)
- [CoRe-Fed: Bridging Collaborative and Representation Fairness via Federated Embedding Distillation](https://mlsanity.com/paper/2602.00647/) (2026)
- [Energy-Efficient Hierarchical Federated Anomaly Detection for the Internet of Underwater Things via Selective Cooperative Aggregation](https://mlsanity.com/paper/2603.24648/) (2026)
- [Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Data](https://mlsanity.com/paper/2608.25794/) (2026)
- [FUSE: Fairness-aware Update Screening against Byzantine Exploitation in q-Fair Federated Learning](https://mlsanity.com/paper/2610.20264/) (2026)

## Abstract (arXiv)

Federated Learning (FL) has emerged as a promising approach for privacy-preserving model training across decentralized devices. However, it faces challenges such as statistical heterogeneity and susceptibility to adversarial attacks, which can impact model robustness and fairness. Personalized FL attempts to provide some relief by customizing models for individual clients. However, it falls short in addressing server-side aggregation vulnerabilities. We introduce a novel method called FedAA, which optimizes client contributions via Adaptive Aggregation to enhance model robustness against malicious clients and ensure fairness across participants in non-identically distributed settings. To achieve this goal, we propose an approach involving a Deep Deterministic Policy Gradient-based algorithm for continuous control of aggregation weights, an innovative client selection method based on model parameter distances, and a reward mechanism guided by validation set performance. Empirically, extensive experiments demonstrate that, in terms of robustness, FedAA outperforms the state-of-the-art methods, while maintaining comparable levels of fairness, offering a promising solution to build resilient and fair federated systems. Our code is available at https://github.com/Gp1g/FedAA.