Group Signature Based Federated Learning in Smart Grids

• Published in: 2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS) pp. 1 - 5
• Main Authors: Kanchan, Sneha, Kumar, Ajit, Saqib, Ali Syed, Choi, Bong Jun
• Format: Conference Proceeding
• Language: English
• Published: IEEE 29.12.2021
• Subjects: Collaborative work; Federated learning; Group signature; Internet security; Privacy preservation; Protocols; Signal processing; Signal processing algorithms; Smart grid; Smart grids; Systematics
• DOI: 10.1109/ICSPIS54653.2021.9729381

Smart Grids are the need of today's energy distribution system, which maintains a systematic communication between suppliers and consumers. Often these grids need to communicate to the Human Machine Interface (HMI) server regarding their findings of the customer needs and availability. However, some external entities might compromise the HMI server, which tends to misuse smart grids' personal information. Hence, the grids should not reveal their or their customer's identity to the server. Federated Learning (FL) can solve this situation where the data from various smart grids can be collected without disclosing the grid's identity. We have proposed a group signature-based federated signature-based in which grid components sign with the group signature instead of their personal signatures. We have verified the security of our algorithm with the Automated Validation of Internet Security Protocols and Applications (AVISPA) simulator.