QSplitFL: Capability Aware Deep Q-Learning for Optimal Split Point Selection in Split Federated Learning
arXiv:2606.09869v1 Announce Type: new Abstract: Federated Learning (FL) combined with Split Learning (SL) is a privacy preserving paradigm that enables training deep neural networks (DNNs) on resource constrained devices while reducing overall training cost. However, determining the optimal split point, meaning the layer where the model is divided still remains a critical challenge, especially when clients have heterogeneous hardware capabilities. Fixed split points can overload weak devices and increase the communication and server load, which slows convergence and reduces stability. This pap
This research addresses a critical challenge in federated learning that becomes more prominent as AI deployments push towards edge devices with heterogeneous capabilities.
Optimizing split point selection in Split Federated Learning can significantly improve efficiency, reduce costs, and broaden the applicability of AI on resource-constrained hardware, accelerating distributed AI adoption.
The ability to dynamically and optimally manage model splitting based on client capabilities will make federated learning more robust and efficient across diverse, real-world environments.
- · Edge AI providers
- · IoT device manufacturers
- · Distributed AI platforms
- · Sectors with sensitive data
- · Centralized cloud AI services (relative)
- · Inefficient distributed AI frameworks
Increased real-world deployment rates of federated and split learning applications.
Reduced operational costs and improved performance for AI systems running on edge devices.
Accelerated development of privacy-preserving AI solutions across industries, potentially impacting data governance and regulatory landscapes.
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Read at arXiv cs.LG