Adaptive Compression in Federated Learning to Enhance Communication Efficiency under Dynamic Bandwidth Conditions

Core Concepts

A communication-efficient federated learning algorithm that adaptively compresses local models based on predicted client bandwidth to improve overall efficiency under dynamic network conditions.

Abstract

The paper proposes a communication-efficient federated learning algorithm called AdapComFL that addresses the challenges of dynamic and heterogeneous client bandwidth in federated learning. Key highlights: Each client predicts its own bandwidth based on collected data and adaptively compresses its local model gradient accordingly before uploading to the server. The paper improves the traditional sketch compression mechanism by fixing the number of columns but elastically adjusting the number of rows based on the predicted bandwidth. This helps maintain accuracy while reducing the upload data volume. The server aggregates the sketch models of different sizes by first aligning the sizes, then linearly accumulating and calculating the row-wise averages. Experiments on real bandwidth data and benchmark datasets show that AdapComFL achieves more efficient communication compared to existing algorithms like FedAvg and SketchFL, while maintaining competitive model accuracy.

Stats

The average real bandwidth (Raw BW) and predicted bandwidth (Pre BW) for each client are similar, with an error around 0.5 MB.

Quotes

"To achieve the communication efficiency of federated learning, there are two categories of approaches: (1) reducing the frequency of total communication by increasing the amount of local computation, (2) reducing the volume of messages each round of communication." "Remarkably, existing methods of achieving communication efficiency in federated learning ignore two problems. First, the network state of each client changes dynamically, as shown by changes in bandwidth. Second, bandwidth of clients is different."

Key Insights Distilled From

Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth

by Ying Zhuansu... at arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03248.pdf

Deeper Inquiries

How can the proposed adaptive compression technique be extended to other distributed learning frameworks beyond federated learning

The proposed adaptive compression technique in federated learning can be extended to other distributed learning frameworks by incorporating similar principles of bandwidth awareness and prediction, as well as dynamic compression based on network conditions. For instance, in a multi-party computation setting, where multiple parties collaborate to perform computations without revealing their individual inputs, adaptive compression can help optimize communication efficiency by adjusting the compression level based on the bandwidth constraints of each party. Similarly, in a decentralized machine learning environment, where nodes in a network collectively train a model, adaptive compression can be utilized to reduce communication overhead while maintaining model accuracy. By integrating the adaptive compression approach into various distributed learning frameworks, the overall efficiency and performance of the systems can be significantly enhanced.

What are the potential security and privacy implications of the sketch-based compression approach, and how can they be further addressed

The sketch-based compression approach, while effective in reducing communication costs in federated learning, may pose security and privacy risks if not implemented carefully. One potential implication is the leakage of sensitive information through the compressed sketch models, especially if the compression process is not adequately secure. To address these concerns, additional measures can be taken such as incorporating encryption techniques to protect the sketch models during transmission and storage. Furthermore, implementing differential privacy mechanisms can help anonymize the data before compression, ensuring that individual contributions remain confidential. Regular audits and monitoring of the compression process can also help identify and mitigate any potential security vulnerabilities. By prioritizing data security and privacy in the sketch-based compression approach, the overall integrity of the federated learning system can be maintained.

Can the bandwidth prediction model be improved by incorporating additional contextual information about the client devices and network conditions

The bandwidth prediction model can be improved by incorporating additional contextual information about the client devices and network conditions. One way to enhance the accuracy of bandwidth prediction is to consider factors such as device type, network congestion levels, and historical bandwidth data. By analyzing the specific characteristics of each client device, such as processing power and network capabilities, the prediction model can tailor its estimates more accurately. Moreover, integrating real-time network monitoring data into the prediction model can provide up-to-date information on network conditions, enabling more precise bandwidth predictions. By leveraging a combination of device-specific details and network context, the bandwidth prediction model can be refined to better adapt to dynamic network environments and optimize communication efficiency in federated learning scenarios.