Results 51 to 60 of about 12,973 (156)

Privacy Protection in Prosumer Energy Management Based on Federated Learning

IEEE Access, 2021
With the booming development of prosumers, there is an urgent need for a prosumer energy management system to take full advantage of the flexibility of prosumers and take into account the interests of other parties.
Yunfeng Li, Xiaoling Li, Gangqiang Li, Zhitao Li   +3 more
doaj   +1 more source

Federated PAC-Bayesian Learning on Non-IID Data

ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Existing research has either adapted the Probably Approximately Correct (PAC) Bayesian framework for federated learning (FL) or used information-theoretic PAC-Bayesian bounds while introducing their theorems, but few considering the non-IID challenges in FL. Our work presents the first non-vacuous federated PAC-Bayesian bound tailored for non-IID local
Zihao Zhao 0001, Yang Liu 0165, Wenbo Ding 0001, Xiao-Ping Zhang 0003   +3 more
openaire   +2 more sources

Fed-TDA: Federated Tabular Data Augmentation on Non-IID Data

CoRR, 2022
Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance ...
Shaoming Duan, Chuanyi Liu, Peiyi Han, Tianyu He, Yifeng Xu, Qiyuan Deng   +5 more
openaire   +2 more sources

Federated Learning for Sentiment Analysis in Presence of Non-IID Data: Sensitivity of Deep Learning Models

IEEE Access
In sentiment analysis, data are commonly distributed across many devices, and traditional machine learning requires transferring these data to a central location exposing data to security and privacy risks. Federated Learning (FL) avoids this transfer by
Davoud Gholamiangonabadi, Katarina Grolinger   +1 more
doaj   +1 more source

A Distributed Privacy Preserved Federated Learning Approach for Revolutionizing Pneumonia Detection in Isolated Heterogenous Data Silos [PDF]

International Journal of Mathematical, Engineering and Management Sciences
Pneumonia is a respiratory lung contamination that ranges in severity from mild to lethal outcomes. The analysis of tomographic images is the most significant method of pneumonia detection.
Shagun Sharma, Kalpna Guleria
doaj   +1 more source

Overcoming Forgetting in Federated Learning on Non-IID Data

CoRR, 2019
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt a solution for catastrophic forgetting to Federated Learning. We add a penalty term to the loss function, compelling all local models to converge to a shared optimum. We show
Neta Shoham, Tomer Avidor, Aviv Keren, Nadav Israel, Daniel Benditkis, Liron Mor-Yosef, Itai Zeitak   +6 more
openaire   +2 more sources

ProFed: A Benchmark for Proximity-Based Non-IID Federated Learning

Journal of Open Research Software
Federated Learning (FL) has emerged as a key paradigm in machine learning but its performance often deteriorates under non-independent and identically distributed (non-IID) client data.
Davide Domini, Christian Otte Ingemann, Gianluca Aguzzi, Lukas Esterle, Mirko Viroli   +4 more
doaj   +1 more source

Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning

Computers
Federated learning (FL), a decentralized approach to machine learning, facilitates model training across multiple devices, ensuring data privacy. However, achieving a delicate privacy preservation–model convergence balance remains a major problem ...
Huda Kadhim Tayyeh, Ahmed Sabah Ahmed AL-Jumaili   +1 more
doaj   +1 more source

Adding Data Quality to Federated Learning Performance Improvement

IEEE Access
Massive data generation from Internet of Things (IoT) devices increases the demand for efficient data analysis to extract relevant and actionable insights. As a result, Federated Learning (FL) allows IoT devices to collaborate in Artificial Intelligence (
Ernesto Gurgel Valente Neto, Solon Alves Peixoto, Valderi Reis Quietinho Leithardt, Juan Francisco de Paz Santana, Julio C. S. Dos Anjos   +4 more
doaj   +1 more source

The Non-IID Data Quagmire of Decentralized Machine Learning

CoRR, 2019
Many large-scale machine learning (ML) applications need to perform decentralized learning over datasets generated at different devices and locations. Such datasets pose a significant challenge to decentralized learning because their different contexts result in significant data distribution skew across devices/locations.
Kevin Hsieh, Amar Phanishayee, Onur Mutlu, Phillip B. Gibbons   +3 more
openaire   +3 more sources