Zhang, Jiangjiang, Waqas, Muhammad ORCID: https://orcid.org/0000-0003-0814-7544, Tu, Shanshan, Badshah, Akhtar, Loukas, George ORCID: https://orcid.org/0000-0003-3559-5182 and Khan, Muhammad Taimoor ORCID: https://orcid.org/0000-0002-5752-6420 (2026) Adaptive and robust personalized federated learning with knowledge-driven logits interaction for secure edge intelligence. In: 2026 IEEE 103rd Vehicular Technology Conference: VTC2026-Spring. 9-12 June 2026. Nice, France. IEEE Xplore . Institute of Electrical and Electronics Engineers (IEEE) - Computer Society - Systems, Man, and Cybernetics Society, Piscataway, New Jersey. (In Press)

PDF (Author's Accepted Manuscript) 53301 WAQAS_Adaptive_And_Robust_Personalized_Federated_Learning_With_Knowledge-Driven_Logits_(AAM)_2026.pdf - Accepted Version Restricted to Repository staff only Download (2MB) | Request a copy

Abstract

As edge intelligence increasingly requires collaborative training, data-driven decision-making, and privacy protection, personalized federated learning (PFL) has become pivotal in
addressing data silos and safeguarding sensitive information. Yet, existing models struggle with generalization, privacy adaptability across heterogeneous clients, and communication efficiency. To overcome these challenges, a Personalized Trusted Federated Learning (PT-FL) algorithm is proposed utilizing a logit interaction architecture and knowledge cache. PT-FL minimizes communication overhead by avoiding direct parameter exchange between devices, thereby enhancing privacy on edge devices. The server-side knowledge cache captures recent insights from each client’s private data, guiding client model updates and effectively mitigating Byzantine attacks and collusion. We have formally verified a set of invariants that assures correctness of asynchronous and concurrent cache operations, enabling safe, scalable, and composable deployment of our neighbor-based inference caching systems. Additionally, knowledge distillation via the cache supports personalized client model training. An extensive experiment demonstrate PT-FL’s superior performance. Under normal conditions, PT-FL achieves high accuracy with convergence 1.8 times faster than comparative algorithms. Moreover, under adversarial conditions, PT-FL maintains accuracy at 72.8%, surpassing baseline methods by 19.7% to 74.6%, thus validating its robustness against attacks through adaptive contamination suppression.

Item Type:	Conference Proceedings
Title of Proceedings:	2026 IEEE 103rd Vehicular Technology Conference: VTC2026-Spring. 9-12 June 2026. Nice, France
Additional Information:	This is the accepted version of the paper published in [2026 IEEE 103rd Vehicular Technology Conference: VTC2026-Spring. 9-12 June 2026. Nice, France]. The final authenticated version is not available as yet. © IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords:	edge intelligence, personalized federated learning, trusted privacy-preserving, knowledge cache, knowledge distillation
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Related URLs:	Organisation
Last Modified:	01 May 2026 12:24
URI:	https://gala.gre.ac.uk/id/eprint/53301

Actions (login required)

View Item

Downloads