Convergence Time Optimization for Federated Learning over Wireless Networks (2001.07845v2)

Abstract: In this paper, the convergence time of federated learning (FL), when deployed over a realistic wireless network, is studied. In particular, a wireless network is considered in which wireless users transmit their local FL models (trained using their locally collected data) to a base station (BS). The BS, acting as a central controller, generates a global FL model using the received local FL models and broadcasts it back to all users. Due to the limited number of resource blocks (RBs) in a wireless network, only a subset of users can be selected to transmit their local FL model parameters to the BS at each learning step. Moreover, since each user has unique training data samples, the BS prefers to include all local user FL models to generate a converged global FL model. Hence, the FL performance and convergence time will be significantly affected by the user selection scheme. Therefore, it is necessary to design an appropriate user selection scheme that enables users of higher importance to be selected more frequently. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize the FL convergence time while optimizing the FL performance. To solve this problem, a probabilistic user selection scheme is proposed such that the BS is connected to the users whose local FL models have significant effects on its global FL model with high probabilities. Given the user selection policy, the uplink RB allocation can be determined. To further reduce the FL convergence time, artificial neural networks (ANNs) are used to estimate the local FL models of the users that are not allocated any RBs for local FL model transmission at each given learning step, which enables the BS to enhance its global FL model and improve the FL convergence speed and performance.

Convergence Time Optimization for Federated Learning over Wireless Networks

The paper "Convergence Time Optimization for Federated Learning over Wireless Networks" by Mingzhe Chen et al. addresses the intersection of federated learning (FL) and wireless networks, with particular emphasis on minimizing convergence time and training loss. The authors explore how the implementation of FL in wireless networks necessitates novel approaches to user selection and resource allocation due to the inherent constraints of wireless communications.

In the setting considered by the authors, federated learning is deployed wherein wireless users send their locally trained models to a central base station (BS). The BS aggregates these models into a global model and broadcasts it back, thus enabling collaborative model training without needing to access users' raw data, addressing privacy concerns. A critical challenge highlighted is the limitation on the number of resource blocks (RBs), which means not all users can transmit their models simultaneously. Hence, the strategy for selecting users to transmit models and the allocation of network resources becomes a key factor affecting convergence time.

The paper formulates this scenario as an optimization problem aimed at minimizing convergence time and training loss. The novelty lies in the proposed probabilistic user selection scheme. The base station probabilistically selects users such that those whose models have significant influence on the global model are chosen more frequently. This selection is coupled with resource allocation strategies to determine how limited bandwidth is distributed among users.

Additionally, the paper discusses the use of artificial neural networks (ANNs) as a novel method to estimate the local models of users not participating in a given round due to resource limitations. This prediction allows the global model to benefit from approximate models even from unselected users, under certain error constraints, thus potentially reducing convergence time and enhancing model accuracy.

The results in this paper are primarily evaluated through simulations, showing that the proposed approach can notably reduce convergence time by up to 56% and improve accuracy by up to 3% in identifying handwritten digits compared to conventional FL algorithms.

Implications and Future Directions

This research has both theoretical and practical implications. Theoretically, it offers a formalized approach to tackling FL challenges specific to wireless networks through optimization and machine learning techniques. Practically, it is of considerable interest for applications where data privacy is paramount, such as autonomous vehicles and IoT networks.

For future developments, exploring dynamic and adaptive algorithms that can quickly react to changing network conditions would be beneficial. Further research could delve into the scalability of the proposed methods and the potential to generalize this approach across different network topologies and learning tasks. Moreover, collaborative techniques where multiple base stations share information globally, not just locally aggregated models, could be another avenue for investigation.

In sum, this paper makes an important contribution to the efficient deployment of federated learning over wireless networks, offering solutions that balance privacy, resource limitations, and convergence efficiency.