Abstract
In federated learning, distributed nodes train a local machine learning model and exchange it through a central aggregator. In real environments, these training nodes are heterogeneous in computing capacity and bandwidth, thus their specific characteristics influence the performance of the federated learning process. We propose for such situations the design of a federated learning server that is able to adapt dynamically to the heterogeneity of the training nodes. In experiments with real devices deployed in a wireless mesh network, we observed that the designed adaptive federated learning server successfully exploited the idle times of the fast nodes by assigning them larger training workloads, which led to a higher global model performance without increasing the training time.
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Notes
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guifi.net: Commons Telecommunication Network Open Free Neutralhttp://guifi.net/.
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Chest X-Ray Images. https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia.
References
Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans. Intell. Syst. Technol. 10(2):1–9
Niknam S, Dhillon HS, Reed JH (2020) Federated learning for wireless communications: motivation, opportunities, and challenges. IEEE Commun Mag 58(6):46–51
Ibraimi L, Selimi M, Freitag F (2021) Bepoch: Improving federated learning performance in resource-constrained computing devices. In: IEEE Global Communications Conference (GLOBECOM)
Freitag F, Vilchez P, Wei L, Liu CH, Selimi M (2022) Performance evaluation of federated learning over wireless mesh networks with low-capacity devices. In: Rocha Á, Ferrás C, Méndez Porras A, Jimenez Delgado E (eds) Information Technology and Systems. Springer International Publishing, Cham, pp 635–645
Llisterri Giménez N, Monfort Grau M, Pueyo Centelles R, Freitag F (2022) On-device training of machine learning models on microcontrollers with federated learning. Electronics 11(4):573
Jiang J, Hu L, Hu C, Liu J, Wang Z (2020) Bacombo-bandwidth-aware decentralized federated learning. Electronics 9(3):440
Abreha HG, Hayajneh M, Serhani MA (2022) Federated learning in edge computing: a systematic survey. Sensors 22(2):450
Mathur A, et al (2021) On-device federated learning with flower
Wang S et al (2019) Adaptive federated learning in resource constrained edge computing systems. IEEE J Sel Areas Commun 37(6):1205–1221
Wang Y, Lin L, Chen J (2022) Communication-efficient adaptive federated learning
Xu H, Li J, Xiong H, Lu H (2020) Fedmax: enabling a highly-efficient federated learning framework. In: 2020 IEEE 13th International Conference on Cloud Computing (CLOUD), pp. 426–434
Parareda EY (2021) Federated learning network: Training distributed machine learning models with the federated learning paradigm
Acknowledgment
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871582 - NGIatlantic.eu and was partially supported by the Spanish Government under contracts PID2019-106774RB-C21, PCI2019-111851-2 (LeadingEdge CHIST-ERA), PCI2019-111850-2 (DiPET CHIST-ERA), and by national funds through FCT, Fundação para a Ciência e a Tecnologia, Portugal, under project UIDB/50021/2020. The work of C.-H. Liu was supported in part by the U.S. National Science Foundation (NSF) under Award CNS-2006453 and in part by Mississippi State University under Grant ORED 253551-060702. The work of L. Wei is supported in part by the U.S. National Science Foundation (#2006612 and #2150486).
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Freitag, F., Wei, L., Liu, CH., Selimi, M., Veiga, L. (2023). Server-side Adaptive Federated Learning over Wireless Mesh Network. In: Rocha, Á., Ferrás, C., Ibarra, W. (eds) Information Technology and Systems. ICITS 2023. Lecture Notes in Networks and Systems, vol 692. Springer, Cham. https://doi.org/10.1007/978-3-031-33261-6_25
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DOI: https://doi.org/10.1007/978-3-031-33261-6_25
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