Articles
Abstract
Federated Learning (FL) allows the model to be trained cooperatively and retain the locality of data, but the exchange of model updates between dispersed clients and a central server can be compromised and manipulated. In this paper, a secure communication system of federated learning integrating quantum key distribution with the use of the BB84 protocol to enhance the establishment of secure keys is introduced. The suggested structure will achieve the use of QKD-generated symmetric keys along with classical cryptography tools to secure the privacy and integrity of model update messages. Federated learning communication and key generation, authentication, and encryption overheads are modeled at a system-level by developing a simulation environment. Parameters introduced to the simulation include the model update size, the number of clients, the communication latency and the cost of key refresh, which allows a more detailed examination of the system performance and feasibility. The findings show that the introduction of QKD is associated with limited and deterministic overhead on the communication latency and key management, and no convergence of the learning process is lost. This paper will concentrate on communication-layer security and will not consider the learning-layer threats e.g., adversarial model poisoning or client malicious behavior. These results show that quantum-secured key exchange can be integrated into federated learning systems and emphasize that it is a key enabling technology of secure communication in distributed learning systems in the future.
