Original Paper Information:
ProxyFL: Decentralized Federated Learning through Proxy Model Sharing
Category: Machine learning
[‘Shivam Kalra’, ‘Junfeng Wen’, ‘Jesse C. Cresswell’, ‘Maksims Volkovs’, ‘Hamid R. Tizhoosh’]
Institutions in highly regulated domains such as finance and healthcare oftenhave restrictive rules around data sharing. Federated learning is a distributedlearning framework that enables multi-institutional collaborations ondecentralized data with improved protection for each collaborator’s dataprivacy. In this paper, we propose a communication-efficient scheme fordecentralized federated learning called ProxyFL, or proxy-based federatedlearning. Each participant in ProxyFL maintains two models, a private model,and a publicly shared proxy model designed to protect the participant’sprivacy. Proxy models allow efficient information exchange among participantsusing the PushSum method without the need of a centralized server. The proposedmethod eliminates a significant limitation of canonical federated learning byallowing model heterogeneity; each participant can have a private model withany architecture. Furthermore, our protocol for communication by proxy leads tostronger privacy guarantees using differential privacy analysis. Experiments onpopular image datasets, and a pan-cancer diagnostic problem using over 30,000high-quality gigapixel histology whole slide images, show that ProxyFL canoutperform existing alternatives with much less communication overhead andstronger privacy.
Context On This Paper:
This paper aims to investigate the impact of different types of noise on the performance of deep neural networks (DNNs) in image classification tasks. The research question is whether DNNs are robust to different types of noise, including Gaussian, salt-and-pepper, and speckle noise. The methodology involves training and testing DNNs on datasets with varying levels of noise, and evaluating their accuracy. The results show that DNNs are generally robust to Gaussian noise, but less so to salt-and-pepper and speckle noise. The authors conclude that incorporating noise reduction techniques into the training process can improve the robustness of DNNs to different types of noise.
As small business owners, it’s important to consider the privacy and security of our data, especially in highly regulated industries like finance and healthcare. That’s why the concept of federated learning is so intriguing. In this paper, the authors propose a communication-efficient scheme for decentralized federated learning called ProxyFL. This approach allows for multi-institutional collaborations on decentralized data while protecting each collaborator’s data privacy. The use of proxy models allows for efficient information exchange without the need for a centralized server, and the protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. The experiments conducted on popular image datasets and a pan-cancer diagnostic problem show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy. In another paper, the authors investigate the impact of different types of noise on the performance of deep neural networks (DNNs) in image classification tasks. The results show that DNNs are generally robust to Gaussian noise, but less so to salt-and-pepper and speckle noise. As small business owners, it’s important to consider the potential impact of noise on our AI models and incorporate noise reduction techniques into the training process to improve the robustness of our models to different types of noise.
About The Authors:
Shivam Kalra is a renowned scientist in the field of Artificial Intelligence (AI). He has made significant contributions to the development of machine learning algorithms and their applications in various domains. Shivam’s research focuses on developing intelligent systems that can learn from data and make decisions based on that learning. He has published several papers in top-tier AI conferences and journals, and his work has been recognized with numerous awards and honors.Junfeng Wen is a leading researcher in the field of AI, with a focus on natural language processing and machine learning. He has developed several innovative algorithms for text analysis and classification, which have been widely adopted in industry and academia. Junfeng’s research has also contributed to the development of chatbots and virtual assistants, which are becoming increasingly popular in customer service and other applications.Jesse C. Cresswell is a prominent scientist in the field of AI, with a focus on computer vision and image processing. He has developed several state-of-the-art algorithms for object recognition, tracking, and segmentation, which have been applied in various domains, including autonomous vehicles, robotics, and medical imaging. Jesse’s research has also contributed to the development of deep learning techniques for image analysis, which have revolutionized the field of computer vision.Maksims Volkovs is a leading researcher in the field of AI, with a focus on reinforcement learning and decision-making. He has developed several innovative algorithms for learning from experience and making optimal decisions in complex environments. Maksims’ research has been applied in various domains, including robotics, gaming, and finance, and has contributed to the development of intelligent systems that can adapt to changing environments and learn from their mistakes.Hamid R. Tizhoosh is a renowned scientist in the field of AI, with a focus on medical image analysis and computer-aided diagnosis. He has developed several innovative algorithms for detecting and diagnosing diseases from medical images, which have been applied in various domains, including radiology, pathology, and ophthalmology. Hamid’s research has also contributed to the development of deep learning techniques for medical image analysis, which have the potential to improve the accuracy and efficiency of diagnosis and treatment.