Power to the Clients: Federated Learning in a Dictatorship Setting

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, extended

Summary

Researchers Mohammadsajad Alipour and Mohammad Mohammadi Amiri from Rensselaer Polytechnic Institute introduce "dictator clients" in federated learning (FL), a new class of malicious participants capable of entirely erasing the contributions of all other clients from the server model while preserving their own. They propose concrete attack strategies for single and collaborative dictator clients, systematically analyzing their effects on the learning process. The study also explores complex scenarios, including multiple dictator clients collaborating, acting independently, or forming alliances to betray one another. Theoretical algorithms and findings are supported by empirical evaluations on computer vision benchmarks like MNIST and CIFAR10, and natural language processing tasks using the AG news dataset with a DistilBERT model. Experiments with five clients in a non-IID setting demonstrate that dictator clients can achieve 0.00% accuracy on non-dictator data while maintaining high accuracy on their own.

Key takeaway

For CTOs and VPs of Engineering overseeing federated learning deployments, you must prioritize advanced anomaly detection and Byzantine-robust aggregation mechanisms. The emergence of "dictator clients" and their ability to unilaterally control or corrupt global models, even through internal betrayal, poses a significant risk to model integrity and fairness. Your teams should investigate and deploy defenses that go beyond traditional Byzantine fault tolerance to specifically counter influence-preserving attacks, ensuring model impartiality and preventing catastrophic learning failures in multi-client environments.

Key insights

Dictator clients can unilaterally control federated learning models, erasing other participants' contributions.

Principles

• FL is vulnerable to malicious clients biasing global models.
• Collaboration among dictator clients enhances attack effectiveness.
• Mutual domination leads to complete model learning failure.

Method

Malicious clients compute and send specific updates to the server, effectively canceling out other clients' gradient contributions and preserving only their own, even enabling betrayal within collaborative groups.

In practice

• Implement robust aggregation rules to detect anomalous updates.
• Monitor client contributions for significant deviations.
• Consider dynamic trust mechanisms for client participation.

Topics

• Federated Learning
• Dictator Clients
• Byzantine Attacks
• Model Poisoning
• Collaborative Attacks

Best for: Research Scientist, CTO, VP of Engineering/Data, AI Scientist, AI Security Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.