Mohamad Wazzeh , Ahmad Hammoud , Azzam Mourad , Hadi Otrok , Chamseddine Talhi , Zbigniew Dziong , Chang-Dong Wang , Mohsen Guizani
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引用次数: 0
Abstract
Efficient resource utilization in Internet of Things (IoT) systems is challenging due to device limitations. These limitations restrict on-device machine learning (ML) model training, leading to longer processing times and inefficient metadata analysis. Additionally, conventional centralized data collection poses privacy concerns, as raw data has to leave the device to the server for processing. Combining Federated Learning (FL) and Split Learning (SL) offers a promising solution by enabling effective machine learning on resource-constrained devices while preserving user privacy. However, the dynamic nature of IoT resources and device heterogeneity can complicate the application of these solutions, as some IoT devices cannot complete the training task on time. To address these concerns, we have developed a Dynamic Split Federated Learning (DSFL) architecture that dynamically adjusts to the real-time resource availability of individual clients. Combining real-time split-point selection with a Genetic Algorithm (GA) for client selection, tailored to heterogeneous, resource-constrained IoT devices. DSFL ensures optimal utilization of resources and efficient training across heterogeneous IoT devices and servers. Our architecture detects each IoT device’s training capabilities by identifying the number of layers it can train. Moreover, an effective Genetic Algorithm (GA) process strategically selects the clients required to complete the split federated learning round. Cooperatively, the clients and servers train their parts of the model, aggregate them, and then combine the results before moving to the next round. Our proposed architecture enables collaborative model training across devices while preserving data privacy by combining FL’s parallelism with SL’s dynamic modeling. We evaluated our architecture on sensory and image-based datasets, showing improved accuracy and reduced overhead compared to baseline methods.
期刊介绍:
Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms.
Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.