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Asynchronous Decentralized Federated Lifelong Learning for Landmark Localization in Medical Imaging
Machine Learning · Submitted 12 Mar 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In conclusion, we developed an ADFLL framework with excellent performance and speed-up compared to conventional RL agents.
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The abstract, sentence by sentence
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized global model to consolidate individual models into one, and the devices train synchronously, which both can be potential bottlenecks for using federated learning. In this paper, we propose a novel method of asynchronous decentralized federated lifelong learning (ADFLL) method that inherits the merits of federated learning and can train on multiple tasks simultaneously without the need for a central node or synchronous training. Thus, overcoming the potential drawbacks of conventional federated learning. We demonstrate excellent performance on the brain tumor segmentation (BRATS) dataset for localizing the left ventricle on multiple image sequences and image orientation. Our framework allows agents to achieve the best performance with a mean distance error of 7.81, better than the conventional all-knowing agent's mean distance error of 11.78, and significantly (p=0.01) better than a conventional lifelong learning agent with a distance error of 15.17 after eight rounds of training. In addition, all ADFLL agents have comparable or better performance than a conventional LL agent. In conclusion, we developed an ADFLL framework with excellent performance and speed-up compared to conventional RL agents.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | However, this framework still requires a centralized global model to consolidate individual models into one, and the devices train synchronously, which both can be potential bottlenecks for using federated learning. | gap 0.64 | 0.07 | 0.02 | hedged possibility 0.65 |
| s2 | In this paper, we propose a novel method of asynchronous decentralized federated lifelong learning (ADFLL) method that inherits the merits of federated learning and can train on multiple tasks simultaneously without the need for a central node or synchronous training. | contribution 0.97 | 0.75 | 0.13 | asserted 1.00 |
| s3 | Thus, overcoming the potential drawbacks of conventional federated learning. | interpretation 0.59 | 0.22 | 0.09 | asserted 0.87 |
| s4 | We demonstrate excellent performance on the brain tumor segmentation (BRATS) dataset for localizing the left ventricle on multiple image sequences and image orientation. | result 0.57 | 0.92 | 0.07 | asserted 1.00 |
| s5 | Our framework allows agents to achieve the best performance with a mean distance error of 7.81, better than the conventional all-knowing agent's mean distance error of 11.78, and significantly (p=0.01) better than a conventional lifelong learning agent with a distance error of 15.17 after eight rounds of training. | result 1.00 | 0.93 | 0.20 | asserted 1.00 |
| s6 | In addition, all ADFLL agents have comparable or better performance than a conventional LL agent. | result 0.99 | 0.90 | 0.08 | asserted 1.00 |
| s7 | In conclusion, we developed an ADFLL framework with excellent performance and speed-up compared to conventional RL agents. | contribution 0.60 | 0.90 | 0.91 | asserted 1.00 |
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Original abstract
Federated learning is a recent development in the machine learning area that allows a system of devices to train on one or more tasks without sharing their data to a single location or device. However, this framework still requires a centralized global model to consolidate individual models into one, and the devices train synchronously, which both can be potential bottlenecks for using federated learning. In this paper, we propose a novel method of asynchronous decentralized federated lifelong learning (ADFLL) method that inherits the merits of federated learning and can train on multiple tasks simultaneously without the need for a central node or synchronous training. Thus, overcoming the potential drawbacks of conventional federated learning. We demonstrate excellent performance on the brain tumor segmentation (BRATS) dataset for localizing the left ventricle on multiple image sequences and image orientation. Our framework allows agents to achieve the best performance with a mean distance error of 7.81, better than the conventional all-knowing agent's mean distance error of 11.78, and significantly (p=0.01) better than a conventional lifelong learning agent with a distance error of 15.17 after eight rounds of training. In addition, all ADFLL agents have comparable or better performance than a conventional LL agent. In conclusion, we developed an ADFLL framework with excellent performance and speed-up compared to conventional RL agents.
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Asynchronous decentralized federated learning has also been explored [ref] .
Continual Learning for Peer-to-Peer Federated Learning: A Study on Automated Brain Metastasis Identification · Conclusion · 0.84
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