BrainTorrent: A Peer-to-Peer Environment for Decentralized Federated Learning
arXiv:1905.06731
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.
References in corpus (3)
Cited by in corpus (31)
- Decentralized Federated Learning: Fundamentals, State of the Art, Frameworks, Trends, and Challenges
- Federated Learning with Cooperating Devices: A Consensus Approach for Massive IoT Networks
- Transformers in Healthcare: A Survey
- A Decentralized Federated Learning Framework via Committee Mechanism with Convergence Guarantee
- Edge Intelligence: Architectures, Challenges, and Applications
- IBM Federated Learning: an Enterprise Framework White Paper V0.1
- A Framework for Evaluating Gradient Leakage Attacks in Federated Learning
- Fedstellar: A Platform for Decentralized Federated Learning
- Federated Learning for Healthcare Informatics
- Communication Efficiency in Federated Learning: Achievements and Challenges
- A blockchain-orchestrated Federated Learning architecture for healthcare consortia
- Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings
- A Survey on Federated Learning and its Applications for Accelerating Industrial Internet of Things
- Decentralized federated learning of deep neural networks on non-iid data
- BAFFLE : Blockchain Based Aggregator Free Federated Learning
- A Systematic Literature Review on Federated Learning: From A Model Quality Perspective
- GFL: A Decentralized Federated Learning Framework Based On Blockchain
- Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients
- Communication-Efficient Training Workload Balancing for Decentralized Multi-Agent Learning
- Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
- Federated Learning over Wireless Device-to-Device Networks: Algorithms and Convergence Analysis
- A(DP)SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy
- Failure Prediction in Production Line Based on Federated Learning: An Empirical Study
- Generation of Synthetic Electronic Health Records Using a Federated GAN
- Robustness of Decentralised Learning to Nodes and Data Disruption
- Confined Gradient Descent: Privacy-preserving Optimization for Federated Learning
- Have you forgotten? A method to assess if machine learning models have forgotten data
- PPT: A Privacy-Preserving Global Model Training Protocol for Federated Learning in P2P Networks
- FedDropoutAvg: Generalizable federated learning for histopathology image classification
- FLRA: A Reference Architecture for Federated Learning Systems
- MAcPNN: Mutual Assisted Learning on Data Streams with Temporal Dependence