arXiv:2607.08013v1 Announce Type: new Abstract: Federated Learning (FL) empowers multiple clients to collaboratively learn a model, enlarging the training data of each client for high accuracy while protecting data privacy. However, when deploying FL in real-time edge systems, the heterogeneity of devices among systems has a severe impact on the performance of the inferred model. Existing optimizations on FL focus on improving the training efficiency but fail to speed up inference, especially when there is a latency constraint. In this work, we propose Collate, a novel training framework that
The rapid deployment of AI at the edge and in real-time systems necessitates frameworks that can overcome device heterogeneity and latency constraints, which existing federated learning solutions do not adequately address.
This work introduces a novel approach to federated learning specifically designed for latency-critical edge systems, which is crucial for the efficient and widespread adoption of AI in diverse real-world applications.
The focus shifts from merely improving federated learning training efficiency to also optimizing inference speed in heterogeneous, latency-constrained edge environments, enabling more robust AI deployment.
- · Edge AI developers
- · IoT device manufacturers
- · Real-time AI applications
- · Federated Learning adoption
- · Inefficient edge AI frameworks
- · Centralized cloud inference
- · Applications with strict latency requirements using traditional FL
Collate could significantly enhance the performance and reliability of AI models deployed on diverse edge devices.
This improved performance at the edge could accelerate the development and deployment of more sophisticated AI applications across industries like autonomous vehicles and industrial IoT.
Widespread adoption of such frameworks might decentralize AI compute, potentially reducing reliance on large data centers for certain types of inferencing and empowering more local AI capabilities.
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Read at arXiv cs.LG