Privacy-Preserving Serverless Federated Learning Scheme for Internet of Things
Changti Wu, Lei Zhang, Lin Xu, Kim‐Kwang Raymond Choo, Liangyu Zhong
Abstract
Federated learning (FL) when deployed in an Internet of Things (IoT) ecosystem can facilitate the collaborative training of a global model involving different IoT local systems. However, there are a number of challenges in such deployments, and examples include single point of failure / attack, lack of fault tolerance, vulnerability to collusion attacks and accuracy loss. Therefore, we propose a privacy-preserving serverless FL scheme for IoT based on secure multi-party computation. Specifically, in our scheme, no central sever is required to coordinate the generation of global models. In doing so, we avoid the single point of failure / attack limitation. We also mitigate the fault tolerance limitation by using secret sharing. Finally, we provide a formal security proof that demonstrates the resilience of our scheme against collusion attacks, thereby establishing its effectiveness in achieving robust data privacy. Simulations are also implemented to show that our scheme does not suffer from accuracy loss.