SHIELDED: A Network-Aware Approach for Secure and Trustworthy Federated Learning

Distributed machine learning, including federated learning (FL), is increasingly taking place <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">within</i> the networks, in the so-called cloud-edge-device continuum. This creates significant challenges, e.g., the need to locate the best resources and data to leverage; at the same time, it also brings novel opportunities to boost the performance of learning and its resiliency to external attacks. In this paper, we present a novel architecture called SHIELDED that enables the decision-making entities in charge of learning orchestration, FL security, and model calibration to: 1) work in an integrated manner and 2) exploit the additional information available in programmable networking scenarios. Our performance evaluation, using network intrusion detection as a case study, shows that SHIELDED yields about 50% better accuracy and 35% lower calibration error compared to present-day alternatives.