Energy Efficient Federated Learning Over Wireless Communication Networks (1911.02417v2)

Abstract: 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.

Overview of "Energy Efficient Federated Learning Over Wireless Communication Networks"

The paper addresses the critical issue of energy-efficient transmission and computation resource allocation in federated learning (FL) over wireless communication networks. The paper presents a model whereby each user device trains a local FL model using its own data and subsequently transmits it to a base station (BS) for aggregation into a global model. This process entails both computational and communication challenges due to energy and latency constraints.

Key Contributions

1. Problem Formulation: The authors frame the challenge as an optimization problem aimed at minimizing the total energy consumption of the system while adhering to latency constraints. This encompasses both computational energy at the user level and energy utilized for data transmission to the BS.
2. Iterative Algorithm: An iterative algorithm is proposed to address the optimization problem. At each iteration, it derives closed-form solutions for time allocation, bandwidth allocation, power control, computation frequency, and learning accuracy, facilitating efficient resource distribution.
3. Completion Time Minimization: The need for an initial feasible solution for the primary optimization problem is met by constructing a completion time minimization problem. The authors introduce a bisection method to derive a feasible solution efficiently.
4. Energy Reduction: Demonstrated through simulations, the proposed algorithms achieve a significant energy reduction of up to 59.5% compared to conventional FL methods, indicating substantial improvements in energy efficiency.

Methodological Insights

• Convergence Analysis: The paper includes a thorough analysis of the convergence rate of the FL algorithm, taking into account the accuracy of local computations and the aggregation processes at the BS. This provides a theoretical framework for understanding the interplay between learning performance and resource allocation.
• Local and Global Iterations: By exploring the trade-offs between local computation accuracy and the number of global iterations, the paper provides insights into optimizing the FL process for energy efficiency.

Practical and Theoretical Implications

• Enhanced Energy Efficiency: The research presents a pathway toward implementing FL in energy-constrained scenarios, such as mobile and IoT devices. By minimizing energy consumption, the approach facilitates the deployment of FL in a sustainable manner.
• Scalability: The low complexity of the proposed algorithm, characterized by linear growth concerning the number of users, suggests that it is scalable and suitable for large-scale applications.
• Broader Impact: The findings may influence further research in adaptive resource allocation in FL, fostering developments that accommodate varying network conditions and user requirements.

Future Directions

• Application to Nonconvex Scenarios: Extending the methodology to address nonconvex loss functions could unlock broader applications in real-world scenarios where nonconvexity is prevalent.
• Integration with Emerging Technologies: Exploring the integration of this approach with technologies such as 5G/6G networks or edge computing frameworks could enhance its applicability and effectiveness.

In conclusion, this paper makes a substantial contribution to the field of federated learning by addressing critical challenges in energy-efficient resource allocation over wireless networks. Its technically rigorous approach and promising results offer a solid foundation for future advancements in energy-conscious FL deployments.