( = Paper PDF, = Presentation slides, = Presentation video)
1.
Mikael Sabuhi; Petr Musilek; Cor-Paul Bezemer
Micro-FL: A Fault-Tolerant Scalable Microservice Based Platform for Federated Learning Journal Article
Future Internet, 16 (3), pp. 1-19, 2024.
Abstract | BibTeX | Tags: Federated learning, Machine learning, Microservices
@article{Sabuhi_MicroFL,
title = {Micro-FL: A Fault-Tolerant Scalable Microservice Based Platform for Federated Learning},
author = {Mikael Sabuhi and Petr Musilek and Cor-Paul Bezemer },
year = {2024},
date = {2024-02-19},
journal = {Future Internet},
volume = {16},
number = {3},
pages = {1-19},
abstract = {As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.},
keywords = {Federated learning, Machine learning, Microservices},
pubstate = {published},
tppubtype = {article}
}
As the number of machine learning applications increases, growing concerns about data privacy expose the limitations of traditional cloud-based machine learning methods that rely on centralized data collection and processing. Federated learning emerges as a promising alternative, offering a novel approach to training machine learning models that safeguards data privacy. Federated learning facilitates collaborative model training across various entities. In this approach, each user trains models locally and shares only the local model parameters with a central server, which then generates a global model based on these individual updates. This approach ensures data privacy since the training data itself is never directly shared with a central entity. However, existing federated machine learning frameworks are not without challenges. In terms of server design, these frameworks exhibit limited scalability with an increasing number of clients and are highly vulnerable to system faults, particularly as the central server becomes a single point of failure. This paper introduces Micro-FL, a federated learning framework that uses a microservices architecture to implement the federated learning system. It demonstrates that the framework is fault-tolerant and scalable, showing its ability to handle an increasing number of clients. A comprehensive performance evaluation confirms that Micro-FL proficiently handles component faults, enabling a smooth and uninterrupted operation.
2.
Mohammad Reza Taesiri; Giang Nguyen; Sarra Habchi; Cor-Paul Bezemer; Anh Nguyen
ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification Inproceedings
NeurIPS Dataset and Benchmark track, 2023.
BibTeX | Tags: Benchmark, Computer vision, Dataset, Image classification, Machine learning
@inproceedings{TaesiriNeurIPS2023,
title = {ImageNet-Hard: The Hardest Images Remaining from a Study of the Power of Zoom and Spatial Biases in Image Classification},
author = {Mohammad Reza Taesiri and Giang Nguyen and Sarra Habchi and Cor-Paul Bezemer and Anh Nguyen},
year = {2023},
date = {2023-12-07},
urldate = {2023-12-07},
booktitle = {NeurIPS Dataset and Benchmark track},
keywords = {Benchmark, Computer vision, Dataset, Image classification, Machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}