# Towards Federated Learning With Byzantine-Robust Client Weighting

arXiv: https://arxiv.org/abs/2004.04986 · HTML: https://mlsanity.com/paper/2004.04986/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2020.

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

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [A Secure Federated Learning Framework for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2209.14547/) (2022)
- [Clustered Federated Learning based on Nonconvex Pairwise Fusion](https://mlsanity.com/paper/2211.04218/) (2022)
- [Robust Quantity-Aware Aggregation for Federated Learning](https://mlsanity.com/paper/2205.10848/) (2022)
- [A Comprehensive Study on Model Initialization Techniques Ensuring Efficient Federated Learning](https://mlsanity.com/paper/2311.02100/) (2023)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by a central server. By assigning a weight to each client based on the proportion of data instances it possesses, the rate of convergence to an accurate joint model can be greatly accelerated. Some previous works studied FL in a Byzantine setting, in which a fraction of the clients may send arbitrary or even malicious information regarding their model. However, these works either ignore the issue of data unbalancedness altogether or assume that client weights are apriori known to the server, whereas, in practice, it is likely that weights will be reported to the server by the clients themselves and therefore cannot be relied upon. We address this issue for the first time by proposing a practical weight-truncation-based preprocessing method and demonstrating empirically that it is able to strike a good balance between model quality and Byzantine robustness. We also establish analytically that our method can be applied to a randomly selected sample of client weights.