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- W4315865188 abstract "Numerous research recently proposed integrating Federated Learning (FL) to address the privacy concerns of using machine learning in privacy-sensitive firms. However, the standards of the available frameworks can no longer sustain the rapid advancement and hinder the integration of FL solutions, which can be prominent in advancing the field. In this paper, we propose ModularFed, a research-focused framework that addresses the complexity of FL implementations and the lack of adaptability and extendability in the available frameworks. We provide a comprehensive architecture that assists FL approaches through well-defined protocols covering three dominant FL paradigms: adaptable workflow, datasets distribution, and third-party application support. Within this architecture, protocols are blueprints that strictly define the framework’s components’ design, contribute to its flexibility, and strengthen its infrastructure. Further, our protocols aim to enable modularity in FL, supporting third-party plug-and-play architecture and dynamic simulators coupled with major built-in data distributors. Additionally, the framework support wrapping multiple approaches in a single environment to enable consistent replication of FL issues such as clients’ deficiency, data distribution, and network latency, which entails a fair comparison of techniques outlying FL technologies. In our evaluation, we examine the applicability of our framework addressing major FL domains, including statistical distribution and modular-based resource monitoring tools and client selection. Moreover, our comparison analysis indicates that our architecture has an inconsiderable impact on performance compared to other approaches." @default.
- W4315865188 created "2023-01-13" @default.
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- W4315865188 date "2023-07-01" @default.
- W4315865188 modified "2023-09-26" @default.
- W4315865188 title "ModularFed: Leveraging modularity in federated learning frameworks" @default.
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- W4315865188 doi "https://doi.org/10.1016/j.iot.2023.100694" @default.
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