Results 51 to 60 of about 10,022,386 (288)
IEEE AccessFederated Learning is a promising paradigm for sharing Cyber Threat Intelligence (CTI) without privacy issues by leveraging the cross-silos data in Software Defined Networking (SDN).
Syed Hussain Ali Kazmi +4 more
doaj +1 more source
A Collaborative Privacy Preserved Federated Learning Framework for Pneumonia Detection using Diverse Chest X-ray Data Silos [PDF]
International Journal of Mathematical, Engineering and Management SciencesPneumonia detection from chest X-rays remains one of the most challenging tasks in the traditional centralized framework due to the requirement of data consolidation at the central location raising data privacy and security concerns.
Shagun Sharma, Kalpna Guleria
doaj +1 more source
Memory attacks in network nonlocality and self-testing [PDF]
QuantumWe study what can or cannot be certified in communication scenarios where the assumption of independence and identical distribution (iid) between experimental rounds fails.
Mirjam Weilenmann +2 more
doaj +1 more source
Decoupled Federated Learning for ASR with Non-IID Data
Interspeech 2022, 2022 Automatic speech recognition (ASR) with federated learning (FL) makes it possible to leverage data from multiple clients without compromising privacy. The quality of FL-based ASR could be measured by recognition performance, communication and computation costs. When data among different clients are not independently and identically distributed (non-IID)
Han Zhu 0004 +4 more
openaire +3 more sources
Social Image Analysis From a Non-IID Perspective [PDF]
IEEE Transactions on Multimedia, 2014 An image in social media, termed a social image, exhibits characteristics different from images widely discussed in image processing. They can be described by both content and social related attributes, called social image attributes, including visual contents, users, tags, and timestamps.
Zhe Xu 0003, Ya Zhang 0002, Longbing Cao
openaire +2 more sources
Continual Learning for Multimodal Data Fusion of a Soft Gripper
Advanced Robotics Research, EarlyView.Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley +1 more source
Balancing Privacy and Performance: A Differential Privacy Approach in Federated Learning
ComputersFederated 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 +1 more
doaj +1 more source
Beyond IID: Learning to combine Non-IID metrics for vision tasks
, 2017 Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Metric learning has been widely employed, especially in various computer vision tasks, with the fundamental assumption that all samples (e.g.
Gao, Y +9 more
core +1 more source
Improving Accuracy of Federated Learning in Non-IID Settings
CoRR, 2020 Federated Learning (FL) is a decentralized machine learning protocol that allows a set of participating agents to collaboratively train a model without sharing their data. This makes FL particularly suitable for settings where data privacy is desired.
Mustafa Safa Özdayi +2 more
openaire +3 more sources
Advanced Science, EarlyView.Streptococcal mannose phosphotransferase system component IID (Man‐PTSIID) is identified as a novel RANK‐binding osteoclastogenic factor. By directly binding to RANK and activating NF‐κB independently of TLR2, Man‐PTSIID drives osteoclastogenesis and inflammatory bone destruction, uncovering an unexpected microbial mechanism underlying streptococcal ...
Chaeyeon Park +13 more
wiley +1 more source