ORCID

Abstract

Federated Learning (FL) has recently emerged as a promising paradigm for privacy-preserving, distributed machine learning. However, FL systems face significant security threats, particularly from adaptive adversaries capable of modifying their attack strategies to evade detection. One such threat is the presence of Reconnecting Malicious Clients (RMCs), which exploit FL’s open connectivity by reconnecting to the system with modified attack strategies. To address this vulnerability, we propose the integration of Identity-Based Identification (IBI) as a security measure within FL environments. By leveraging IBI, we enable FL systems to authenticate clients based on cryptographic identity schemes, effectively preventing previously disconnected malicious clients from re-entering the system. Our approach is implemented using the TNC-IBI (Tan-Ng-Chin) scheme over elliptic curves to ensure computational efficiency, particularly in resource-constrained environments like the Internet of Things (IoT). Experimental results demonstrate that integrating IBI with secure aggregation algorithms, such as Krum and Trimmed Mean, significantly improves FL robustness by mitigating the impact of RMCs. We further discuss the broader implications of IBI in FL security, highlighting research directions for adaptive adversary detection, reputation-based mechanisms, and the applicability of identity-based cryptographic frameworks in decentralised FL architectures. Our findings advocate for a holistic approach to FL security, emphasising the necessity of proactive defence strategies against evolving adaptive adversarial threats.

Publication Date

2025-09-12

Publication Title

IEEE Access

Volume

13

ISSN

2169-3536

Acceptance Date

2025-09-03

Deposit Date

2025-11-13

Funding

This work was supported by the Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme under Grant FRGS/1/2023/ICT07/MMU/03/1.

Keywords

Machine learning, adaptive adversaries, federated learning, identity-based identification, secure aggregation

First Page

176024

Last Page

176036

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