MSFL: Model-Safeguarded Federated Learning With Intelligent Reflecting Surface for Industrial Networks
Yingying Wu, Bomin Mao, Nei Kato · IEEE Transactions on Network and Service Management · 2024
Industry 4.0 generates a huge volume of data, where Federated Learning (FL) can be utilized to mine the data in a privacy-preserving manner. However, traditional FL in privacy-preserving is not sufficient, the uploaded local model gradients can be intercepted by external Eavesdroppers (Eve), with more enough of which the users’ raw data can be inferred, leading to privacy leakage. At the same time, the future 6G accommodating more devices, makes privacy concerns sharper. To tackle privacy issues in FL, in this paper, we propose a Model-Safeguarded FL framework based on Intelligent Reflecting Surface (IRS) (MSFL) where Non-Orthogonal Multiple Access (NOMA) is introduced to enable multiple devices access. Specifically, IRS is deployed between Base Station (BS) and participants, improving the wireless environment and preventing Eve from eavesdropping. The Deep Deterministic Policy Gradient (DDPG)-Optimized Power and Phase (DOPP) algorithm is proposed to jointly optimize transmission power at participants and IRS phase shift to maximize the minimum confidentiality capacity. Extensive results demonstrate that the maximum confidentiality capacity of our MSFL scheme is up to 1.7 bps/Hz at a transmission rate of 30 dBW, which is approximately 300% more than that of the Block Coordinate Ascent Method (BCAM) and Artificial Noise (AN).