federated machine learning research papers

Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteeing the data privacy of the trainers. FedAvg has become a foundational parameter update strategy for federated learning, which has been promising to eliminate the effect of the heterogeneous data across clients and ...

We focus on the problem of Personalized Federated Continual Learning (PFCL): a group of distributed clients, each with a sequence of local tasks on arbitrary data distributions, collaborate through a central server to train a personalized model at each client, with the model expected to achieve good performance on all local tasks. We propose a novel PFCL framework called Federated Memory ...

Federated learning (FL) is a type of distributed machine learning that allows multiple clients to collaboratively train a machine learning model without uploading their own private data. However, the distributed nature of FL makes it vulnerable to Byzantine attacks.

This study is a review of FL applications that address various agricultural problems and compares the types of data partitioning, types of FL, architectures, levels of federation, and the use of aggregation algorithms in different reviewed approaches and applications of FL in agriculture. Federated learning (FL), with the aim of training machine learning models using data and computational ...

For this special issue we invited authors of papers that appeared at ACSAC 2022 and that successfully passed an evaluation of their software and/or data artifacts to submit an extended version of their papers. This selection criteria ensured that the research has a high potential for being deployed in real-world environments and to be used to ...

Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. Recently, artificial neural networks have been able to surpass many previous approaches in performance. Machine learning approaches have been applied ...

Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift ...

Federated Learning (FL) as a promising distributed machine learning paradigm has been widely adopted in Artificial Intelligence of Things (AIoT) applications. However, the efficiency and inference capability of FL is seriously limited due to the presence of stragglers and data imbalance across massive AIoT devices, respectively. To address the above challenges, we present a novel asynchronous ...

Federated learning (FL) serves as a decentralized training framework for machine learning (ML) models, preserving data privacy in critical domains such as smart healthcare. However, it has been found that attackers can exploit this decentralized learning framework to perform data and model poisoning attacks, specifically in FL-driven smart ...

Machine learning An Inductive Inference Machine. Ray Solomonoff; IRE Convention Record, Section on Information Theory, Part 2, pp. 56-62, 1957 (A longer version of this, a privately circulated report, 1956, is online). Description: The first paper written on machine learning. Emphasized the importance of training sequences, and the use of ...

Cognitive learning is progressively prospering in the field of Internet of Things (IoT). With the advancement in IoT, data generation rate has also increased, whereas issues like performance, attacks on the data, security of the data, and inadequate data resources are yet to be resolved. Recent studies are mostly focusing on the security of the data which can be handled by machine learning.

The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.

In this paper, we propose Federated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modules. First, the sub-FM construction module employs a layer-wise compression approach, facilitating comprehensive FM fine-tuning across all layers by emphasizing those crucial neurons. Second, the ...

Our findings indicate that machine learning models, particularly MLP, significantly outperformed traditional strategies, achieving higher annualized excess returns and Sharpe ratios. Machine learning's ability to capture complex patterns and leverage both volume and price data proved crucial, especially in managing higher frequency trading that ...

Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic de-velopment; inconsistent dataset and model usage make fair algorithm comparison challenging. In this work, we introduce FedML, an open research library and bench-

This paper introduces a new framework for federated auxiliary hard-parameter sharing multi-task learning, namely, FedAuxHMTL. The introduced framework incorporates model parameter exchanges between edge server and base stations, enabling base stations from distributed areas to participate in the FedAuxHMTL process and enhance the learning ...

Although Split Federated Learning (SFL) is good at enabling knowledge sharing among resource-constrained clients, it suffers from the problem of low training accuracy due to the neglect of data heterogeneity and catastrophic forgetting. To address this issue, we propose a novel SFL approach named KoReA-SFL, which adopts a multi-model aggregation mechanism to alleviate gradient divergence ...