UVeQFed: Universal Vector Quantization for Federated Learning (2006.03262v3)

Abstract: Traditional deep learning models are trained at a centralized server using labeled data samples collected from end devices or users. Such data samples often include private information, which the users may not be willing to share. Federated learning (FL) is an emerging approach to train such learning models without requiring the users to share their possibly private labeled data. In FL, each user trains its copy of the learning model locally. The server then collects the individual updates and aggregates them into a global model. A major challenge that arises in this method is the need of each user to efficiently transmit its learned model over the throughput limited uplink channel. In this work, we tackle this challenge using tools from quantization theory. In particular, we identify the unique characteristics associated with conveying trained models over rate-constrained channels, and propose a suitable quantization scheme for such settings, referred to as universal vector quantization for FL (UVeQFed). We show that combining universal vector quantization methods with FL yields a decentralized training system in which the compression of the trained models induces only a minimum distortion. We then theoretically analyze the distortion, showing that it vanishes as the number of users grows. We also characterize the convergence of models trained with the traditional federated averaging method combined with UVeQFed to the model which minimizes the loss function. Our numerical results demonstrate the gains of UVeQFed over previously proposed methods in terms of both distortion induced in quantization and accuracy of the resulting aggregated model.

• The paper introduces a subtractive dithered lattice quantization method that significantly lowers communication overhead in federated learning.
• The paper provides convergence analysis showing that UVeQFed matches unquantized federated learning performance under diverse data conditions.
• The paper demonstrates that UVeQFed efficiently scales by reducing quantization error and communication cost, benefiting large-scale FL systems.

Universal Vector Quantization for Federated Learning (UVeQFed)

The paper "UVeQFed: Universal Vector Quantization for Federated Learning" addresses a significant challenge in federated learning (FL) systems related to communication efficiency. Federated learning is a technique that enables the training of machine learning models without the need to centralize data from users, thereby preserving privacy and reducing communication costs. However, one of the principal difficulties in FL is the transmission of model updates from users to the central server over bandwidth-constrained communication links. This paper introduces a novel quantization approach named Universal Vector Quantization for Federated Learning (UVeQFed), designed to mitigate this communication overhead while maintaining the quality of the learned model.

Key Aspects of UVeQFed

1. Universal Quantization Approach: The core of UVeQFed is its utilization of subtractive dithered lattice quantization, a method known for its robustness across varying data distributions. This technique involves adding and subsequently subtracting a random dither signal to the data, allowing for effective quantization without prior knowledge of the data's statistical properties. This characteristic is especially useful in federated learning settings where model updates from end devices have unpredictable distributions.
2. Independence from Statistical Models: UVeQFed aligns well with federated learning's decentralized nature by operating effectively without assuming any specific statistical model of the data. Traditional quantization methods often rely on such assumptions to achieve efficient compression, which can be limiting in practice.
3. Reduction of Quantization Noise: The paper demonstrates that the quantization error introduced by UVeQFed is considerably smaller compared to conventional methods. The effectiveness of dithered quantization ensures that the expected quantization distortion diminishes as the number of users increases, an important property for federated systems that grow in scale.
4. Scalability and Convergence: A significant theoretical contribution of the paper is the convergence analysis of UVeQFed. The authors prove that federated averaging with UVeQFed converges at the same order as unquantized federated learning, even under heterogeneous data distributions and limited communication budgets. This ensures that the technique can be reliably scaled to large networks and datasets, maintaining model quality despite bandwidth constraints.

Numerical Results

The authors have conducted extensive experiments, testing UVeQFed against popular benchmarks such as MNIST and CIFAR-10 datasets. The results show that UVeQFed surpasses existing replication and quantization methods, including scalar quantizers and stochastic quantization used in techniques like QSGD. Notably, it achieves similar levels of accuracy with significantly lower communication costs, proving the effectiveness of vector quantization over scalar approaches under identical bit constraints.

Implications and Future Directions

The development of UVeQFed is a substantial step towards optimizing federated learning for real-world deployments. By minimizing communication inefficiencies through effective quantization, this technique promises to facilitate the scalability of federated learning systems into widespread applications such as IoT networks and mobile devices.

In potential future work, the paper points towards exploring further refinements of lattice structures and pursuing adaptive techniques that could dynamically adjust quantization parameters based on network requirements and dataset evolution. Additionally, integrating over-the-air computation and joint source-channel coding with UVeQFed could unlock further efficiencies by directly accounting for the noisy and shared nature of wireless communication channels, a prevalent setting for federated learning systems.

In conclusion, UVeQFed represents a promising technique that balances privacy, communication efficiency, and learning efficacy, vital elements for the ongoing evolution of federated learning.