BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning (1905.06731v1)

Abstract: Access to sufficient annotated data is a common challenge in training deep neural networks on medical images. As annotating data is expensive and time-consuming, it is difficult for an individual medical center to reach large enough sample sizes to build their own, personalized models. As an alternative, data from all centers could be pooled to train a centralized model that everyone can use. However, such a strategy is often infeasible due to the privacy-sensitive nature of medical data. Recently, federated learning (FL) has been introduced to collaboratively learn a shared prediction model across centers without the need for sharing data. In FL, clients are locally training models on site-specific datasets for a few epochs and then sharing their model weights with a central server, which orchestrates the overall training process. Importantly, the sharing of models does not compromise patient privacy. A disadvantage of FL is the dependence on a central server, which requires all clients to agree on one trusted central body, and whose failure would disrupt the training process of all clients. In this paper, we introduce BrainTorrent, a new FL framework without a central server, particularly targeted towards medical applications. BrainTorrent presents a highly dynamic peer-to-peer environment, where all centers directly interact with each other without depending on a central body. We demonstrate the overall effectiveness of FL for the challenging task of whole brain segmentation and observe that the proposed server-less BrainTorrent approach does not only outperform the traditional server-based one but reaches a similar performance to a model trained on pooled data.

• The paper introduces BrainTorrent, which replaces the central server with peer-to-peer interactions to enhance privacy and eliminate single points of failure.
• The experimental results on whole-brain segmentation reveal that BrainTorrent matches or surpasses traditional methods, achieving about a 7% Dice score improvement on heterogeneous data.
• The framework demonstrates significant potential for secure and efficient training in healthcare by overcoming the limitations of centralized federated learning.

BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning

The paper "BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning" presents the development and evaluation of BrainTorrent, an innovative framework that departs from traditional federated learning (FL) by eliminating the reliance on a central server. The key objective of BrainTorrent is to enhance privacy and mitigate the limitations associated with server-dependent FL, especially within the context of medical data.

Federated Learning and Its Challenges

Federated learning represents a paradigm shift in collaborative model training by allowing multiple institutions to build a shared model without directly sharing their datasets. This approach inherently addresses privacy concerns by keeping data localized. Traditional FL employs a central server to coordinate training, whereby each client trains a local model, and the changes are aggregated by the server after a few epochs of local training. Despite its effectiveness, this central-server requirement introduces vulnerability — a single point of failure — and a potential privacy bottleneck, as it assumes that all participating centers trust one central authority.

Introduction of BrainTorrent

BrainTorrent advances the field by introducing a server-less FL framework whereby medical institutions — or clients — engage in a highly dynamic peer-to-peer interaction model. Unlike conventional FL with the requirement of a central server, BrainTorrent capitalizes on the direct interactions between clients, offering a more autonomous and resilient environment. This novel approach is particularly advantageous in medical settings, where the number of participants is typically limited, and the communication infrastructure between clients is robust.

Experimental Demonstration

To demonstrate the efficacy of BrainTorrent, the authors applied the framework to the task of whole-brain segmentation using MRI images, a significantly more complex task compared to the binary segmentations previously tackled in FL research. The experiments utilized the Multi-Atlas Labeling Challenge (MALC) dataset and leveraged the QuickNAT architecture for segmentation. The evaluation consisted of two main experiments:

1. Varying Number of Clients: The results indicated that BrainTorrent either matched or surpassed the performance of traditional FL approaches across different configurations (e.g., 5, 7, 10, and 20 clients), with notable consistency as the number of clients — and thus heterogeneity in data — increased.
2. Non-uniform Data Distribution: Clients received training data with different size and demographic characteristics (e.g., age distributions). BrainTorrent consistently outperformed FLS in this heterogeneous setup, demonstrating ~7% improvement in Dice scores, thereby highlighting its adaptability and robust training across diverse datasets.

Implications and Future Directions

The introduction of BrainTorrent has profound implications for the practical deployment of federated learning frameworks in fields that are especially sensitive to data privacy, such as healthcare. By removing the dependency on a central server, BrainTorrent not only enhances privacy but also reduces the risks associated with server failures and trust issues.

Theoretically, BrainTorrent opens new avenues in distributed machine learning by enabling a more democratized and equitable model training environment. Future work could involve expanding BrainTorrent to other domains requiring similar decentralized approaches, potentially integrating with blockchain technology to further solidify trust and transparency in peer-to-peer communications. Additionally, exploring optimization strategies to minimize communication overheads while maximizing learning efficiency could yield further performance enhancements.

In conclusion, BrainTorrent represents a significant stride in federated learning methodologies by facilitating a decentralized, highly interactive peer-to-peer network, specifically tailored to the intricate and privacy-centric demands of medical imaging. This evolution presents an empowering framework for centers with obligations to stringent data governance while simultaneously advancing the scientific frontier in machine learning.