FedRAD: Federated Robust Adaptive Distillation

• URL: http://arxiv.org/abs/2112.01405v1
• Date: Thu, 2 Dec 2021 16:50:57 GMT
• Title: FedRAD: Federated Robust Adaptive Distillation
• Authors: Stef\'an P\'all Sturluson, Samuel Trew, Luis Mu\~noz-Gonz\'alez, Matei Grama, Jonathan Passerat-Palmbach, Daniel Rueckert, Amir Alansary
• Abstract summary: Collaborative learning framework by typically aggregating model updates is vulnerable to model poisoning attacks from adversarial clients. We propose a novel robust aggregation method, Federated Robust Adaptive Distillation (FedRAD), to detect adversaries and robustly aggregate local models. The results show that FedRAD outperforms all other aggregators in the presence of adversaries, as well as in heterogeneous data distributions.
• Score: 7.775374800382709
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The robustness of federated learning (FL) is vital for the distributed training of an accurate global model that is shared among large number of clients. The collaborative learning framework by typically aggregating model updates is vulnerable to model poisoning attacks from adversarial clients. Since the shared information between the global server and participants are only limited to model parameters, it is challenging to detect bad model updates. Moreover, real-world datasets are usually heterogeneous and not independent and identically distributed (Non-IID) among participants, which makes the design of such robust FL pipeline more difficult. In this work, we propose a novel robust aggregation method, Federated Robust Adaptive Distillation (FedRAD), to detect adversaries and robustly aggregate local models based on properties of the median statistic, and then performing an adapted version of ensemble Knowledge Distillation. We run extensive experiments to evaluate the proposed method against recently published works. The results show that FedRAD outperforms all other aggregators in the presence of adversaries, as well as in heterogeneous data distributions.

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