Bayesian Nonparametric Federated Learning of Neural Networks (1905.12022v1)

Abstract: In federated learning problems, data is scattered across different servers and exchanging or pooling it is often impractical or prohibited. We develop a Bayesian nonparametric framework for federated learning with neural networks. Each data server is assumed to provide local neural network weights, which are modeled through our framework. We then develop an inference approach that allows us to synthesize a more expressive global network without additional supervision, data pooling and with as few as a single communication round. We then demonstrate the efficacy of our approach on federated learning problems simulated from two popular image classification datasets.

• The paper introduces a Bayesian nonparametric approach that combines local neural networks using the Beta-Bernoulli Process to form an effective global model.
• The methodology decouples training and integration, allowing dynamic model complexity and compatibility with pre-trained networks while preserving data privacy.
• The framework reduces communication rounds and computational overhead, achieving superior or competitive accuracy on image classification tasks like MNIST and CIFAR-10.

Bayesian Nonparametric Federated Learning of Neural Networks

This paper presents a methodology for federated learning that leverages Bayesian nonparametric (BNP) techniques, specifically the Beta-Bernoulli Process (BBP), to effectively combine neural networks trained on distinct data silos. The approach addresses common challenges in federated learning, such as privacy concerns and communication costs, by enabling the synthesis of a global model from local models without pooling data.

Methodology Overview

The proposed method utilizes a Bayesian nonparametric framework where neural network weights from individual data sources are combined using a probabilistic matching process. The core of this approach involves modeling local weights through a BBP, enabling discrete selection of shared global parameters based on the local models' observations. This allows for the construction of a more expressive global model with minimal communication, potentially requiring only a single round of interaction between the local nodes and the central server.

Key Contributions

1. Decoupled Learning: The framework separates local model training from their amalgamation into a global model. This decoupling permits flexibility in local training algorithms and compatibility with pre-trained models—addressing scenarios where the original data is unavailable due to legal or practical constraints.
2. Nonparametric Model Growth: Leveraging BNP properties, the framework can dynamically adjust the complexity of the global model to match data intricacies, achieving a balance between model size and performance.
3. Single-Round Communication: The necessity for extensive rounds of communication—a significant bottleneck in traditional federated learning—is minimized, thereby reducing latency and enhancing efficiency.

Experimental Evaluation

The framework was tested on tasks derived from popular image classification datasets, MNIST and CIFAR-10. The experimental results demonstrate that the proposed method surpasses existing federated learning strategies and performs competitively against ensemble approaches, especially under conditions of constrained communications. Notably:

• The method consistently outperformed local models in terms of accuracy.
• It achieved comparable results to ensemble models but with reduced computational and storage overhead.
• The approach proved resilient in both homogeneous and heterogeneous data distributions across batches.

Implications and Future Directions

The implications of this work are particularly relevant for applications where data privacy is paramount, and resource efficiency is crucial. By enabling federated learning with reduced communication and leveraging pre-trained networks, this methodology aligns well with regulatory environments and practical deployment scenarios.

Future research could extend this framework to other neural architectures like CNNs and RNNs, providing generalizability across a wider range of machine learning tasks. Additionally, investigating methods to incorporate partial supervision when merging networks could improve outcomes in cases where initial local models are weak.

In conclusion, the paper advances the federated learning landscape by introducing a BNP approach that offers both theoretical robustness and practical advantages, setting the stage for continued innovation in distributed and privacy-preserving AI systems.