A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks

By Mingzhe Chen, Zhaohui Yang, Walid Saad, Changchuan Yin, H. Vincent Poor, Shuguang Cui. 2019

1,442 citations recorded by the source corpus.

In this article, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that generates a global FL model and sends the model back to the users. Since all training parameters are transmitted over wireless links, the quality of training is affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS needs to select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To seek the solution, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can improve the identification accuracy by up to 1.4%, 3.5% and 4.1%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation, 2) a standard FL algorithm with random user selection and resource allocation, and 3) a wireless optimization algorithm that minimizes the sum packet error rates of all users while being agnostic to the FL parameters.

Subfields: Artificial Intelligence, Computer Networks and Communications, Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Networks and Communications

In Electrical and Electronic Engineering

Citations in the corpus

6G for Vehicle-to-Everything (V2X) Communications: Enabling Technologies, Challenges, and Opportunities (2020, background): “As shown in [174], wireless errors and delays can affect the accuracy of federated learning.”
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].”
A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications (2023, background): “These application scenarios demonstrate the adaptability and potential of XL-MIMO in addressing the challenges and requirements of various systems.”
Communication-Efficient Edge AI: Algorithms and Systems (2020, background): “[187] optimized device selection and wireless resource allocation for federated learning model aggregation over a wireless channel, and derived the convergence rate.”
Federated Learning for Edge Networks: Resource Optimization and Incentive Mechanism (2019, background): “On the other hand, the access network and core network resources must be allocated optimally in the communication phase [10].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “implement edge artificial intelligence (AI) in B5G networks [8], [9], [10], [11], [12], [13].”
Jamming Attacks and Anti-Jamming Strategies in Wireless Networks: A Comprehensive Survey (2021, background): “In the past two decades, we have witnessed the significant advancement of wireless communication and networking technologies such as polar code [1], [2], massive multiple-input multiple-output (MIMO) [3]–[5], millimeter-wave (mmwave) [6], [7], nonorthogonal multiple access (NOMA) [8]–[11], carrier a”
Convergence Time Optimization for Federated Learning Over Wireless Networks (2020, background): “The works in [6] and [7] optimized the FL performance with communication constraints such as limited spectrum and computational resources.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “Speciﬁcally, for edge training systems, we need to jointly optimize the subcarrier and bandwidth allocation [241], [88], [90], transmit power and receive beamforming [31], [48], [178], passive beamforming at RIS [48], [178], device selection [31], [242] and activity detection [76], local updates…”
Multi-Hop RIS-Empowered Terahertz Communications: A DRL-Based Hybrid Beamforming Design (2021, background): “extraordinarily remarkable technology has introduced in recent years [47]–[52].”
Federated Learning for 6G: Applications, Challenges, and Opportunities (2021, background): “One of the most promising distributed machine-learning frameworks is federated learning (FL) [2–19].”
Energy Efficient Federated Learning Over Wireless Communication Networks (2019, methodology): “In [33], we investigated the FL loss function minimization problem with taking into account packet errors over wireless links.”
Federated Learning for Internet of Things: A Comprehensive Survey (2021, methodology): “, the accuracy of an FL-based object classification task [29].”
Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges (2020, methodology): “• From [64], we derived that federated learning must use some effective compression technique due to limited communication resources and a massive number of IoT”