A Joint Learning and Communications Framework for Federated Learning over Wireless Networks (1909.07972v4)

Abstract: In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, 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 will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must 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 address this problem, 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 reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation.

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

In this paper, the authors present a comprehensive paper on the implementation of Federated Learning (FL) algorithms over practical wireless networks. The paper addresses crucial problems associated with integrating FL within wireless settings, including the effects of packet errors, bandwidth limitations, and resource allocation constraints. The proposed framework aims to jointly optimize the learning process and wireless communications, striving to minimize the overall FL loss function under these realistic conditions.

Key Contributions

The authors make several key contributions to the literature on FL in wireless networks:

1. Novel FL Model with Realistic Wireless Considerations: The authors propose an FL model where users perform local model updates using their own data and transmit these models to a base station (BS). The BS aggregates these updates into a global model and broadcasts it back to the users. This model explicitly accounts for wireless impairments like packet errors and bandwidth constraints, which previous studies often neglected.
2. Optimization Formulation Incorporating Wireless Factors: The joint optimization problem formulated in the paper aims to minimize the FL loss function. It incorporates user selection, resource allocation, and power control while adhering to constraints on delay and energy consumption. This holistic approach differentiates the paper from existing works which either focus on FL algorithm improvements or wireless optimizations in isolation.
3. Derivation of Expected Convergence Rate: A significant theoretical contribution is the closed-form expression for the expected convergence rate of the FL algorithm. This expression quantifies the impact of wireless factors, such as packet error rates, on FL convergence, offering insights into how these errors degrade learning performance and speed. The authors build on this to develop optimization strategies that enhance FL performance over unreliable wireless channels.
4. Power Control and Resource Allocation: The work provides closed-form solutions for optimal user transmit power given specific resource allocation. Additionally, the authors employ a Hungarian algorithm to solve the joint user selection and resource allocation problem, thereby minimizing the FL loss function efficiently. This method balances network constraints like packet error rates with FL objectives, a novel approach not extensively explored in prior research.

Numerical Results and Analysis

Simulation results underscore the efficacy of the proposed framework, highlighting several vital findings:

• Performance Enhancement:

The proposed framework significantly improves FL performance compared to baselines that either select users and allocate resources randomly or solely optimize wireless transmission without considering FL parameters. Specifically, the framework achieves up to 1.4% improvement in identification accuracy over an optimal user selection algorithm with random resource allocation, 3.5% over a standard FL algorithm, and 4.1% over wireless optimization approaches agnostic of FL parameters.

• Impact of User and Resource Parameters:

The simulations reveal that the number of users and the size of their local datasets significantly affect FL performance. Increased data samples per user enhance training accuracy, demonstrating diminished returns beyond a threshold. Similarly, more users improve FL accuracy, although gains taper off as bandwidth constraints limit effective user participation.

• Scalability and Complexity:

The proposed approach demonstrates scalability with respect to both user count and available resource blocks. Despite the higher computational complexity of the Hungarian algorithm, it effectively finds optimal solutions for user-resource matching, crucial for handling large-scale networks without prohibitive computational overhead.

Practical and Theoretical Implications

Practically, this paper paves the way for deploying FL in real-world wireless networks, crucial for applications requiring privacy-preserving distributed learning such as collaborative sensing, autonomous driving, and IoT-based smart cities. The framework ensures that FL algorithms remain robust even when subjected to inherent wireless transmission errors and bandwidth limitations.

Theoretically, the derivation of expected convergence rates underpins further research in joint optimization for distributed learning and communication. It offers a foundation for exploring more complex learning models, non-convex loss functions, and advanced wireless technologies like 5G and beyond.

Future Directions

Building upon this framework, future developments could delve into:

• Dynamic adaptation strategies for real-time wireless conditions.
• Robust FL algorithms resilient to varying packet error rates and delays.
• Cross-layer designs integrating more sophisticated machine learning models with ultra-reliable low latency communication (URLLC) protocols.
• Energy-efficient FL frameworks leveraging novel wireless energy harvesting and low-power communication technologies.

In conclusion, this paper provides a rigorous, insightful exploration into the amalgamation of federated learning with wireless communications, laying a foundational framework that substantially improves the practical feasibility and performance of FL algorithms in real-world networks.