Federated Learning via Over-the-Air Computation (1812.11750v3)

Abstract: The stringent requirements for low-latency and privacy of the emerging high-stake applications with intelligent devices such as drones and smart vehicles make the cloud computing inapplicable in these scenarios. Instead, edge machine learning becomes increasingly attractive for performing training and inference directly at network edges without sending data to a centralized data center. This stimulates a nascent field termed as federated learning for training a machine learning model on computation, storage, energy and bandwidth limited mobile devices in a distributed manner. To preserve data privacy and address the issues of unbalanced and non-IID data points across different devices, the federated averaging algorithm has been proposed for global model aggregation by computing the weighted average of locally updated model at each selected device. However, the limited communication bandwidth becomes the main bottleneck for aggregating the locally computed updates. We thus propose a novel over-the-air computation based approach for fast global model aggregation via exploring the superposition property of a wireless multiple-access channel. This is achieved by joint device selection and beamforming design, which is modeled as a sparse and low-rank optimization problem to support efficient algorithms design. To achieve this goal, we provide a difference-of-convex-functions (DC) representation for the sparse and low-rank function to enhance sparsity and accurately detect the fixed-rank constraint in the procedure of device selection. A DC algorithm is further developed to solve the resulting DC program with global convergence guarantees. The algorithmic advantages and admirable performance of the proposed methodologies are demonstrated through extensive numerical results.

Federated Learning via Over-the-Air Computation: An Analytical Overview

The emergence of high-stake applications involving intelligent devices, such as drones and smart vehicles, mandates stringent requirements for low-latency and data privacy that traditional cloud computing cannot satisfy. Consequently, edge machine learning, where training and inference are conducted directly at network edges, is gaining traction. Federated learning extends this concept by enabling the training of machine learning models on decentralized devices. The necessity to address imbalances in non-IID data, while reducing communication overhead, led to the development of the Federated Averaging (FedAvg) algorithm. However, communication bandwidth limitations persist as a bottleneck in aggregating local model updates. This is where the novel approach of over-the-air computation (AirComp) promises significant advancements.

Research Synopsis

The paper "Federated Learning via Over-the-Air Computation" offers an innovative solution to enhance the communication efficiency of federated learning systems by leveraging the principles of AirComp. This method utilizes the signal superposition property of wireless multiple-access channels. The authors propose a joint device selection and beamforming design framework, modeled as a sparse and low-rank optimization problem. The adopted methodology is further refined by using a difference-of-convex-functions (DC) representation to handle the sparse and low-rank functions within the device selection process. A DC algorithm was developed to solve this problem, demonstrating global convergence guarantees.

Key Contributions

1. Integration of AirComp for Federated Learning: The paper designs a novel fast model aggregation approach by utilizing AirComp to exploit the signal superposition property of wireless channels. This integration is aimed at reducing the communication rounds necessary for global model updating, thereby improving the overall efficiency of federated learning systems.
2. Sparse and Low-Rank Optimization: By modeling the joint device selection and beamforming design problem as a sparse and low-rank optimization challenge, the researchers provided a framework conducive to efficient algorithmic solutions. This approach is directed at maximizing the number of devices involved in the learning process under MSE constraints, ensuring an enhanced statistical learning performance.
3. DC Representation Framework: A significant methodological advancement is the introduction of a unified DC representation framework for inducing both sparsity and low-rank structures. This approach is particularly potent in accurately detecting the feasibility of nonconvex quadratic constraints during device selection.
4. Algorithmic Superiority: The developed DC algorithm is notable for its convergence guarantees. The performance evaluations, conducted through extensive numerical experiments, indicate a superior capability of the proposed methodologies in achieving better prediction accuracy and faster convergence rates compared to state-of-the-art approaches.

Numerical Results and Validation

The proposed DC approach was validated through simulations involving a support vector machine (SVM) classifier trained on the CIFAR-10 dataset. The paper compared the training loss and accuracy of models trained using the developed algorithm against other approaches. The results demonstrated the algorithm's ability to select more devices, achieve lower aggregation error, and ensure higher prediction accuracy.

Implications and Future Directions

The implications of this research are manifold, extending from practical applications in real-time intelligent systems to theoretical developments in distributed learning and optimization.

• Practical Implications: The ability to efficiently aggregate models in federated learning scenarios opens up new possibilities for deploying edge AI in latency-sensitive and privacy-aware applications, particularly in autonomous systems and IoT networks.
• Theoretical Implications: The successful application of DC programming in the context of sparse and low-rank optimization problems underscores the potential for further research in this area. This could lead to algorithmic advancements in other convex-concave structured optimization problems pervasive in machine learning and signal processing.

Future Developments in AI

Several promising avenues for future research and development arise from this work:

1. Robustness Against Channel Uncertainty: Investigating the impacts and handling of channel uncertainty in the aggregation process to robustify model updates against real-world wireless channel fluctuations.
2. Security Enhancements: Developing methods to mitigate risks from adversarial attacks during the aggregation process to ensure model robustness and integrity.
3. Optimality Conditions of DC Algorithms: Further theoretical exploration to establish optimality conditions and improve the tightness of DC approximation in practical scenarios.

In summary, the authors' work significantly contributes to the efficiency and effectiveness of federated learning by pioneering the use of over-the-air computation for model aggregation. Their methodologies and findings pave the way for more robust, efficient, and privacy-preserving AI systems implemented at the edge.