# Secure and Private Federated Learning: Achieving Adversarial Resilience through Robust Aggregation

arXiv: https://arxiv.org/abs/2505.17226 · HTML: https://mlsanity.com/paper/2505.17226/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 14 references that are in the map.
- First posted on arXiv 22 May 2025.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Learning from History for Byzantine Robust Optimization](https://mlsanity.com/paper/2012.10333/) (2020)
- [FedGT: Identification of Malicious Clients in Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.05506/) (2023)
- [Krum Federated Chain (KFC): Using blockchain to defend against adversarial attacks in Federated Learning](https://mlsanity.com/paper/2502.06917/) (2025)

## Built on by (most cited first, 1 of 1)

- [FedStrategist: A Meta-Learning Framework for Adaptive and Robust Aggregation in Federated Learning](https://mlsanity.com/paper/2507.14322/) (2025)

## Abstract (arXiv)

Federated Learning (FL) enables collaborative machine learning across decentralized data sources without sharing raw data. It offers a promising approach to privacy-preserving AI. However, FL remains vulnerable to adversarial threats from malicious participants, referred to as Byzantine clients, who can send misleading updates to corrupt the global model. Traditional aggregation methods, such as simple averaging, are not robust to such attacks. More resilient approaches, like the Krum algorithm, require prior knowledge of the number of malicious clients, which is often unavailable in real-world scenarios. To address these limitations, we propose Average-rKrum (ArKrum), a novel aggregation strategy designed to enhance both the resilience and privacy guarantees of FL systems. Building on our previous work (rKrum), ArKrum introduces two key innovations. First, it includes a median-based filtering mechanism that removes extreme outliers before estimating the number of adversarial clients. Second, it applies a multi-update averaging scheme to improve stability and performance, particularly when client data distributions are not identical. We evaluate ArKrum on benchmark image and text datasets under three widely studied Byzantine attack types. Results show that ArKrum consistently achieves high accuracy and stability. It performs as well as or better than other robust aggregation methods. These findings demonstrate that ArKrum is an effective and practical solution for secure FL systems in adversarial environments.