Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness (2004.00490v2)

Abstract: In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. In this paper, a novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the "importance" of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness

The paper presents an intricate exploration of scheduling policies designed to optimize federated edge learning (FEEL) in cellular networks, emphasizing the interplay between communication channels and the significance of local learning updates. Through the research, a novel scheduling strategy is introduced that leverages the diversity inherent in multiuser wireless channels and updates from edge devices, ultimately aiming to enhance learning efficiency without compromising data privacy.

The primary focus of the paper is to facilitate expedited and effective training of neural networks across multiple edge devices. These devices share only their computed learning updates with an access point, rather than raw data, ensuring data privacy. In such a distributed framework, constrained by limited communication resources, the strategic selection of the most informative local learning contributions becomes paramount.

The proposed scheduling policy is built upon a probabilistic framework designed to ensure unbiased aggregation of updates in FEEL setups. The researchers introduce a metric based on gradient divergence to measure the importance of local learning updates. This metric plays a critical role in formulating a probabilistic scheduling mechanism that systematically balances the trade-offs between channel quality and update significance. The core contribution of the scheduling policy is highlighted through its closed-form derivation for scenarios where a single edge device is scheduled per communication round, as well as its extension to cases involving multiple device scheduling.

Strong numerical evidence underpins the superiority of the proposed scheduler. Evaluations conducted using well-established models and datasets demonstrate more rapid model convergence and higher accuracy compared to traditional approaches, which predominantly leverage either channel quality or update importance in isolation.

The implications of this research are far-reaching, particularly in domains requiring intelligent data management and privacy preservation simultaneously. By circumventing direct data transfers between devices and central systems, the framework enhances privacy, potentially gaining traction in sensitive fields such as healthcare and finance. Theoretically, this paper contributes to the growing body of knowledge on resource management and optimization in federated learning contexts. Future research may explore further refinements of the prioritization metrics or adaptations of the scheduling policies to dynamic network environments.

This work builds a solid foundation that could spur advancements in the integration of AI and wireless technologies, aligning with the evolution towards more autonomous and efficient edge networks. It underscores the necessity of harmonizing update significance with channel conditions to optimize the learning potential of federated systems, pivotal in scaling AI deployments at the edge.