Efficient Compression of Federated Learning Model Updates Using Error-Bounded Lossy Compression

Core Concepts

FEDSZ, a specialized lossy compression algorithm, can significantly reduce the size of client model updates in federated learning while maintaining high inference accuracy.

Abstract

The paper introduces FEDSZ, a compression scheme designed to minimize the size of client model updates in federated learning (FL) environments. FEDSZ incorporates a comprehensive compression pipeline featuring data partitioning, lossy and lossless compression of model parameters and metadata, and serialization. The key highlights and insights are: FEDSZ leverages error-bounded lossy compression (EBLC) techniques like SZ2, SZ3, SZx, and ZFP to compress the dense weight parameters of FL models. Experiments show that SZ2 with a relative error bound of 10^-2 achieves an optimal tradeoff, compressing model states between 5.55-12.61× while maintaining inference accuracy within < 0.5% of uncompressed results. FEDSZ uses lossless compression (blosc-lz) for the non-weight metadata and parameters, which account for a small fraction (≈1%) of the model update size. The runtime overhead of FEDSZ is < 4.7% of the wall-clock communication-round time, a worthwhile trade-off for reducing network transfer times by an order of magnitude for networks with bandwidths < 500Mbps. FEDSZ demonstrates effective weak and strong scaling, achieving a recalculated speedup up to 1.64 and 7.51 respectively as the number of clients and CPU cores are increased. The error introduced by FEDSZ's lossy compression could potentially serve as a source of differentially private noise, opening up new avenues for privacy-preserving federated learning.

Stats

The communication time for transmitting AlexNet over a 10Mbps network is reduced from 150 minutes to 13.26 minutes using FEDSZ with a relative error bound of 10^-2. The communication time for transmitting MobileNetV2 and ResNet50 over a 10Mbps network is reduced by 12.23% and 9.74% respectively using FEDSZ with a relative error bound of 10^-2.

Quotes

"FEDSZ, a specialized lossy compression algorithm, can significantly reduce the size of client model updates in federated learning while maintaining high inference accuracy." "SZ2 with a relative error bound of 10^-2 achieves an optimal tradeoff, compressing model states between 5.55-12.61× while maintaining inference accuracy within < 0.5% of uncompressed results." "The runtime overhead of FEDSZ is < 4.7% of the wall-clock communication-round time, a worthwhile trade-off for reducing network transfer times by an order of magnitude for networks with bandwidths < 500Mbps."

Key Insights Distilled From

FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications

by Grant Wilkin... at arxiv.org 04-25-2024

https://arxiv.org/pdf/2312.13461.pdf

Deeper Inquiries

How can the error introduced by FEDSZ's lossy compression be leveraged to provide differential privacy guarantees in federated learning

The error introduced by FEDSZ's lossy compression can be leveraged to provide differential privacy guarantees in federated learning by utilizing the concept of noise injection. In differential privacy, the goal is to add noise to the data in such a way that the statistical properties of the data remain intact while protecting individual privacy. By intentionally introducing controlled errors during the compression process, FEDSZ can act as a source of noise that obscures the original data values. This noise can help in achieving differential privacy guarantees by making it harder for an adversary to extract sensitive information about individual data points. Furthermore, the error introduced by FEDSZ can be calibrated to meet specific privacy requirements, such as epsilon-differential privacy, where the amount of noise added is adjusted based on the desired level of privacy protection. This way, FEDSZ not only reduces communication overhead but also enhances the privacy and security of federated learning systems.

What other techniques could be combined with FEDSZ to further improve the communication efficiency and scalability of federated learning systems

To further improve the communication efficiency and scalability of federated learning systems, FEDSZ can be combined with techniques such as model sparsification, quantization, and encryption. Model Sparsification: By leveraging techniques like gradient sparsification, only significant gradients are transmitted during the communication rounds, reducing the amount of data exchanged between clients and the server. This can complement FEDSZ by reducing the overall communication overhead even further. Quantization: Applying quantization to the model parameters can further reduce the size of the data transmitted between clients and the server. Combining FEDSZ's lossy compression with quantization techniques can lead to significant reductions in communication costs while maintaining model accuracy. Encryption: Implementing encryption mechanisms to secure the compressed data during transmission can enhance the privacy and security of federated learning systems. By encrypting the compressed updates, sensitive information is protected from unauthorized access, ensuring data confidentiality. Adaptive Compression Strategies: Implementing adaptive compression strategies that dynamically adjust the compression techniques based on the network conditions and client resources can optimize communication efficiency in real-time. This adaptive approach can ensure that the most suitable compression method is applied for each communication round, maximizing performance. By integrating these techniques with FEDSZ, federated learning systems can achieve a balance between communication efficiency, scalability, privacy, and security.

What are the potential implications of using FEDSZ in resource-constrained edge devices for real-time applications like autonomous vehicles or IoT systems

Using FEDSZ in resource-constrained edge devices for real-time applications like autonomous vehicles or IoT systems can have several potential implications: Reduced Communication Overhead: FEDSZ's efficient compression algorithms can significantly reduce the amount of data that needs to be transmitted between edge devices and central servers. This can lead to faster communication, lower latency, and reduced network congestion, crucial for real-time applications. Improved Model Update Efficiency: By compressing model updates before transmission, FEDSZ can optimize the use of limited bandwidth and computational resources on edge devices. This ensures that model updates are efficiently communicated without compromising the performance of the learning process. Enhanced Privacy and Security: The differential privacy guarantees provided by FEDSZ's error-introducing compression can enhance the privacy and security of data transmitted from edge devices. This is particularly important in applications like autonomous vehicles, where sensitive information needs to be protected from unauthorized access. Scalability and Adaptability: FEDSZ's scalability and adaptability make it well-suited for edge devices that may have varying computational capabilities and network conditions. The ability to adjust compression strategies based on resource constraints ensures optimal performance in diverse real-time scenarios. Real-time Decision Making: By reducing communication delays through efficient compression, FEDSZ enables faster decision-making in real-time applications. This is critical for autonomous vehicles and IoT systems where timely responses are essential for safety and efficiency. Overall, leveraging FEDSZ in resource-constrained edge devices can lead to improved communication efficiency, enhanced privacy, and optimized performance in real-time applications.