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SparSFA: Towards robust and communication-efficient peer-to-peer federated learning
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0002-2772-4661
Imperial College London, UK.
IBM Research Europe, Ireland.
RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.ORCID iD: 0000-0002-1954-760x
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2023 (English)In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 129, article id 103182Article in journal (Refereed) Published
Abstract [en]

Federated Learning (FL) has emerged as a powerful paradigm to train collaborative machine learning (ML) models, preserving the privacy of the participants’ datasets. However, standard FL approaches present some limitations that can hinder their applicability in some applications. Thus, the need of a server or aggregator to orchestrate the learning process may not be possible in scenarios with limited connectivity, as in some IoT applications, and offer less flexibility to personalize the ML models for the different participants. To sidestep these limitations, peer-to-peer FL (P2PFL) provides more flexibility, allowing participants to train their own models in collaboration with their neighbors. However, given the huge number of parameters of typical Deep Neural Network architectures, the communication burden can also be very high. On the other side, it has been shown that standard aggregation schemes for FL are very brittle against data and model poisoning attacks. In this paper, we propose SparSFA, an algorithm for P2PFL capable of reducing the communication costs. We show that our method outperforms competing sparsification methods in P2P scenarios, speeding the convergence and enhancing the stability during training. SparSFA also includes a mechanism to mitigate poisoning attacks for each participant in any random network topology. Our empirical evaluation on real datasets for intrusion detection in IoT, considering both balanced and imbalanced-dataset scenarios, shows that SparSFA is robust to different indiscriminate poisoning attacks launched by one or multiple adversaries, outperforming other robust aggregation methods whilst reducing the communication costs through sparsification. 

Place, publisher, year, edition, pages
Elsevier Ltd , 2023. Vol. 129, article id 103182
Keywords [en]
Adversarial machine learning, Communication efficiency, IDS, IoT, Peer-to-peer federated learning, Poisoning attack, Cost reduction, Deep neural networks, Internet of things, Learning systems, Network architecture, Network security, Network topology, Communication cost, Machine learning models, Machine-learning, Peer to peer, Poisoning attacks, Intrusion detection
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-64312DOI: 10.1016/j.cose.2023.103182Scopus ID: 2-s2.0-85151480655OAI: oai:DiVA.org:ri-64312DiVA, id: diva2:1752958
Note

Correspondence Address: Wang, H.; RISE Research Institutes of SwedenSweden; email: han.wang@ri.se; Funding details: 830927; Funding details: 101020259; Funding text 1: This research is funded by the EU H2020 projects ARCADIAN-IoT (Grant ID. 101020259) and CONCORDIA (Grant ID: 830927).; Funding text 2: This research is funded by the EU H2020 projects ARCADIAN-IoT (Grant ID. 101020259) and CONCORDIA (Grant ID: 830927).

Available from: 2023-04-25 Created: 2023-04-25 Last updated: 2023-11-06Bibliographically approved

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Wang, HanEklund, DavidRaza, Shahid

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