Federated Learning (FL) represents the de facto approach for distributed training of machine learning models. Nevertheless, researchers have identified several security and privacy FL issues. Among these, the lack of anonymity exposes FL to linkability attacks, representing a risk for model alteration and worker impersonation, where adversaries can explicitly select the attack target, knowing its identity. Named-Data Networking (NDN) is a novel networking paradigm that decouples the data from its location, anonymising the users. NDN embodies a suitable solution to ensure workers’ privacy in FL, thus fixing the abovementioned issues. However, several issues must be addressed to fit FL logic in NDN semantics, such as missing push-based communication in NDN and anonymous NDN naming convention. To this end, this paper contributes a novel anonymous-by-design FL framework with a customised communication protocol leveraging NDN. The proposed communication scheme encompasses an ad-hoc FL-oriented naming convention and anonymity-driven forwarding and enrollment procedures. The anonymity and privacy requirements considered during the framework definition are fully satisfied through a detailed analysis of the framework’s robustness. Moreover, we compare the proposed mechanism and state-of-the-art anonymity solutions, focusing on the communication efficiency perspective. The simulation results show latency and training time improvements up to ∼30%, especially when dealing with large models, numerous federations, and complex networks.

Anonymous Federated Learning via Named-Data Networking / Agiollo, Andrea; Bardhi, Enkeleda; Conti, Mauro; Dal Fabbro, Nicolò; Lazzeretti, Riccardo. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 152:(2023), pp. -303. [10.1016/j.future.2023.11.009]

Anonymous Federated Learning via Named-Data Networking

Bardhi, Enkeleda
Co-primo
;
Conti, Mauro;Lazzeretti, Riccardo
2023

Abstract

Federated Learning (FL) represents the de facto approach for distributed training of machine learning models. Nevertheless, researchers have identified several security and privacy FL issues. Among these, the lack of anonymity exposes FL to linkability attacks, representing a risk for model alteration and worker impersonation, where adversaries can explicitly select the attack target, knowing its identity. Named-Data Networking (NDN) is a novel networking paradigm that decouples the data from its location, anonymising the users. NDN embodies a suitable solution to ensure workers’ privacy in FL, thus fixing the abovementioned issues. However, several issues must be addressed to fit FL logic in NDN semantics, such as missing push-based communication in NDN and anonymous NDN naming convention. To this end, this paper contributes a novel anonymous-by-design FL framework with a customised communication protocol leveraging NDN. The proposed communication scheme encompasses an ad-hoc FL-oriented naming convention and anonymity-driven forwarding and enrollment procedures. The anonymity and privacy requirements considered during the framework definition are fully satisfied through a detailed analysis of the framework’s robustness. Moreover, we compare the proposed mechanism and state-of-the-art anonymity solutions, focusing on the communication efficiency perspective. The simulation results show latency and training time improvements up to ∼30%, especially when dealing with large models, numerous federations, and complex networks.
2023
Federated Learning; Anonymity; Named Data Networking; Privacy-preserving Protocols; Next Generation Communication
01 Pubblicazione su rivista::01a Articolo in rivista
Anonymous Federated Learning via Named-Data Networking / Agiollo, Andrea; Bardhi, Enkeleda; Conti, Mauro; Dal Fabbro, Nicolò; Lazzeretti, Riccardo. - In: FUTURE GENERATION COMPUTER SYSTEMS. - ISSN 0167-739X. - 152:(2023), pp. -303. [10.1016/j.future.2023.11.009]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1692080
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