Byzantine-Robust Aggregation in Federated Learning Empowered Industrial IoT
Shenghui Li, Edith C.‐H. Ngai, Thiemo Voigt · IEEE Transactions on Industrial Informatics · 2021
Federated learning (FL) is a promising paradigm to empower on-device intelligence in Industrial Internet of Things (IIoT) due to its capability of training machine learning models across multiple IIoT devices while preserving the privacy of their local data. However, the distributed architecture of FL relies on aggregating the parameter list from the remote devices, which poses potential security risks caused by malicious devices. In this article, we propose a flexible and robust aggregation rule, called auto-weighted geometric median (AutoGM), and analyze the robustness against outliers in the inputs. To obtain the value ofAutoGM, we design an algorithm based on the alternating optimization strategy. UsingAutoGMas aggregation rule, we propose two robust FL solutionsAutoGM_FLandAutoGM_PFL.AutoGM_FLlearns a shared global model using the standard FL paradigm, andAutoGM_PFLlearns a personalized model for each device. We conduct extensive experiments on the FEMNIST and Bosch IIoT datasets. The experimental results show that our solutions are robust against both model poisoning and data poisoning attacks. In particular, our solutions sustain high performance even when 30% of the nodes perform model or 50% of the nodes perform data poisoning attacks.