EdgeFLow: Serverless Federated Learning via Sequential Model Migration in Edge Networks
arXiv:2603.02562v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data exchanges and long-distance transmissions. This work presents EdgeFLow, an innovative FL framework that redesigns the system topology by replacing traditional cloud servers with sequential model migration between edge base stations. By conducting model aggregation and propagation exclusively at edge clusters, EdgeFLow eliminates cloud-based transmissions and substantially reduces global communication overhead. We provide rigorous convergence analysis for EdgeFLow under non-convex objectives and non-IID data distributions, extending classical FL convergence theory. Experimental results across various configurations validate the theoretical analysis, demonstrating that
arXiv:2603.02562v1 Announce Type: new Abstract: Federated Learning (FL) has emerged as a transformative distributed learning paradigm in the era of Internet of Things (IoT), reconceptualizing data processing methodologies. However, FL systems face significant communication bottlenecks due to inevitable client-server data exchanges and long-distance transmissions. This work presents EdgeFLow, an innovative FL framework that redesigns the system topology by replacing traditional cloud servers with sequential model migration between edge base stations. By conducting model aggregation and propagation exclusively at edge clusters, EdgeFLow eliminates cloud-based transmissions and substantially reduces global communication overhead. We provide rigorous convergence analysis for EdgeFLow under non-convex objectives and non-IID data distributions, extending classical FL convergence theory. Experimental results across various configurations validate the theoretical analysis, demonstrating that EdgeFLow achieves comparable accuracy improvements while significantly reducing communication costs. As a systemic architectural innovation for communication-efficient FL, EdgeFLow establishes a foundational framework for future developments in IoT and edge-network learning systems.
Executive Summary
The article introduces EdgeFLow, a novel serverless federated learning framework that migrates models sequentially between edge base stations, eliminating the need for cloud-based transmissions and reducing global communication overhead. EdgeFLow achieves comparable accuracy improvements while significantly reducing communication costs, making it a foundational framework for future developments in IoT and edge-network learning systems.
Key Points
- ▸ EdgeFLow replaces traditional cloud servers with sequential model migration between edge base stations
- ▸ The framework eliminates cloud-based transmissions and reduces global communication overhead
- ▸ EdgeFLow achieves comparable accuracy improvements while reducing communication costs
Merits
Improved Communication Efficiency
EdgeFLow reduces global communication overhead by conducting model aggregation and propagation exclusively at edge clusters
Enhanced Scalability
The framework's ability to migrate models sequentially between edge base stations enables it to handle large-scale IoT and edge-network learning systems
Demerits
Limited Applicability
EdgeFLow may not be suitable for applications that require real-time processing or have strict latency requirements
Dependence on Edge Infrastructure
The framework's performance is dependent on the quality and availability of edge infrastructure, which can be a limiting factor in certain environments
Expert Commentary
The introduction of EdgeFLow marks a significant advancement in the field of federated learning, as it addresses the long-standing issue of communication bottlenecks in traditional FL systems. By leveraging the strengths of edge computing, EdgeFLow enables efficient and scalable learning systems that can be applied to a wide range of IoT and edge-network applications. However, further research is needed to fully realize the potential of EdgeFLow and to address the limitations and challenges associated with its implementation.
Recommendations
- ✓ Further research is needed to investigate the applicability of EdgeFLow to different IoT and edge-network learning systems
- ✓ The development of EdgeFLow should be accompanied by investments in edge infrastructure and the promotion of edge computing technologies