arXiv:2605.22266v1 Announce Type: new Abstract: Federated learning enables collaborative training across distributed clients with heterogeneous data, but such heterogeneity often leads to unstable updates and degraded global performance. Moreover, in practical deployments, client updates may deviate from the expected behavior not only due to benign not i.i.d. distributions, but also due to distributional shifts or anomalous inputs, raising concerns about the reliability of the aggregation process. In this work, we propose a lightweight geometric signal to quantify the functional deviation of a
The increasing complexity and adoption of federated learning in real-world applications necessitate robust mechanisms for ensuring data integrity and model reliability due to growing concerns about data heterogeneity and potential anomalies.
Detecting atypical clients in federated learning is crucial for maintaining the integrity, performance, and trustworthiness of AI systems, particularly as these systems become more integrated into critical infrastructure and decision-making processes.
This research introduces a novel, lightweight method to identify functional deviations in federated learning clients, offering a more effective way to secure collaborative AI training against diverse forms of data shifts and anomalous inputs.
- · Federated Learning implementers
- · AI security researchers
- · Privacy-preserving AI solutions
- · Industries using distributed AI (healthcare, finance)
- · Malicious actors in federated learning environments
- · AI systems vulnerable to data poisoning
- · Organizations with poor data governance in distributed setups
Improved model robustness and reliability in federated learning deployments.
Accelerated adoption of federated learning in sensitive domains due to enhanced security and trust.
New regulatory frameworks and standards for secure distributed AI development and deployment, potentially impacting global AI supply chains.
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