Energy Efficient Federated Learning Over Wireless Communication Networks

By Zhaohui Yang, Mingzhe Chen, Walid Saad, Choong Seon Hong, Mohammad Shikh-Bahaei. 2019

823 citations recorded by the source corpus.

In this paper, the problem of energy efficient transmission and computation resource allocation for federated learning (FL) over wireless communication networks is investigated. In the considered model, each user exploits limited local computational resources to train a local FL model with its collected data and, then, sends the trained FL model to a base station (BS) which aggregates the local FL model and broadcasts it back to all of the users. Since FL involves an exchange of a learning model between users and the BS, both computation and communication latencies are determined by the learning accuracy level. Meanwhile, due to the limited energy budget of the wireless users, both local computation energy and transmission energy must be considered during the FL process. This joint learning and communication problem is formulated as an optimization problem whose goal is to minimize the total energy consumption of the system under a latency constraint. To solve this problem, an iterative algorithm is proposed where, at every step, closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy are derived. Since the iterative algorithm requires an initial feasible solution, we construct the completion time minimization problem and a bisection-based algorithm is proposed to obtain the optimal solution, which is a feasible solution to the original energy minimization problem. Numerical results show that the proposed algorithms can reduce up to 59.5% energy consumption compared to the conventional FL method.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

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Citations in the corpus

Distributed Learning in Wireless Networks: Recent Progress and Future Challenges (2021, background): “The authors in [50]–[52] study the trade-off between the local ML model updates and global ML model aggregation so as to minimize the total energy consumption for local ML model training and transmission or”
Task Allocation for Mobile Federated and Offloaded Learning with Energy and Delay Constraints (2020, background): “The impact of energy on the validation accuracy has only recently been studied [13], [14].”
Convergence Time Optimization for Federated Learning Over Wireless Networks (2020, background): “In [8] and [9], the authors investigated the tradeoff between the FL convergence time and users’ energy consumption.”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, neutral): “1 [20].”
Towards Federated Learning in UAV-Enabled Internet of Vehicles: A Multi-Dimensional Contract-Matching Approach (2020, methodology): “In particular, following the formulation in [51]:”
A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks (2019, methodology): “FL is a distributed machine learning method that enables users to collaboratively learn a shared prediction model while keeping their collected data on their devices [6]–[10].”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, background): “Centralized Learning Algorithms • K-means [103], [104] • Mixture models [105], [106] • Autoencoders [107] • Generative adversarial network [108], [109] • Support-vector machine [110] • Logistic regression [111] • Deep neural network [107], [112], [113] • Deep Q-learning [114]–[116] • Policy gradient”
Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “Under fading channels with limited bandwidth, the convergence bound has been derived, through which the communication resource allocation has been optimized in [171] and [172].”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “The work in [179] focuses on formulating a joint computation and transmission optimization problem, aiming to minimize the total energy consumption for local computation and wireless transmission in FL-based edge IoT systems.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “Energy Wireless-powered over-the-air FL [91]; energy-efficient FL [92]; green edge inference [53], [54]”
Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2019, neutral): “For example, the study of [124] allows the FL server to calibrate levels of fairness when allocating training importance, whereas the study in [113] enables the tradeoffs between training completion time and energy expense to be calibrated by the FL system.”
Communication-Efficient Edge AI: Algorithms and Systems (2020, methodology): “[206] proposed to optimize wireless resources allocation by studying the trade-off between delay and energy consumption.”