<ul><li>Federated Learning (FL) allows collaborative machine learning without sharing raw data, offering privacy-preserving AI.</li><li>FL faces threats from malicious participants known as Byzantine clients, impacting the global model.</li><li>Average-rKrum (ArKrum) is a new aggregation strategy improving resilience and privacy in FL systems, addressing vulnerabilities.</li><li>ArKrum utilizes a median-based filtering mechanism and multi-update averaging scheme to enhance stability and performance against Byzantine attacks.</li></ul>

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

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