Abstract
Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs); this allowed its development as DNNs proliferated but neglected those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only support DNNs reinforces this problem. To address the lack of non-DNN-based FL solutions, we propose MAFL (Model-Agnostic Federated Learning). MAFL merges a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel® OpenFL. MAFL is the first FL system not tied to any machine learning model, allowing exploration of FL beyond DNNs. We test MAFL from multiple points of view, assessing its correctness, flexibility, and scaling properties up to 64 nodes of an HPC cluster. We also show how we optimised OpenFL achieving a 5.5\(\times \) speedup over a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.
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Acknowledgments
This work has been supported by the Spoke “FutureHPC & BigData" of the ICSC - Centro Nazionale di Ricerca in “High Performance Computing, Big Data and Quantum Computing", funded by European Union - NextGenerationEU and the EuPilot project funded by EuroHPC JU under G.A. n. 101034126.
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Mittone, G., Riviera, W., Colonnelli, I., Birke, R., Aldinucci, M. (2023). Model-Agnostic Federated Learning. In: Cano, J., Dikaiakos, M.D., Papadopoulos, G.A., Pericàs, M., Sakellariou, R. (eds) Euro-Par 2023: Parallel Processing. Euro-Par 2023. Lecture Notes in Computer Science, vol 14100. Springer, Cham. https://doi.org/10.1007/978-3-031-39698-4_26
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