Zobrazeno 1 - 10
of 2 273
pro vyhledávání: '"Zomaya, Albert"'
Autor:
Tavallaie, Omid, Thilakarathna, Kanchana, Seneviratne, Suranga, Seneviratne, Aruna, Zomaya, Albert Y.
Federated Learning (FL) is a distributed machine learning paradigm designed for privacy-sensitive applications that run on resource-constrained devices with non-Identically and Independently Distributed (IID) data. Traditional FL frameworks adopt the
Externí odkaz:
http://arxiv.org/abs/2409.15067
Autor:
Nazemi, Niousha, Tavallaie, Omid, Chen, Shuaijun, Mandalari, Anna Maria, Thilakarathna, Kanchana, Holz, Ralph, Haddadi, Hamed, Zomaya, Albert Y.
Federated Learning (FL) is a promising distributed learning framework designed for privacy-aware applications. FL trains models on client devices without sharing the client's data and generates a global model on a server by aggregating model updates.
Externí odkaz:
http://arxiv.org/abs/2409.01722
Autor:
Nazemi, Niousha, Tavallaie, Omid, Mandalari, Anna Maria, Haddadi, Hamed, Holz, Ralph, Zomaya, Albert Y.
This paper investigates the impact of internet centralization on DNS provisioning, particularly its effects on vulnerable populations such as the indigenous people of Australia. We analyze the DNS dependencies of Australian government domains that se
Externí odkaz:
http://arxiv.org/abs/2408.12958
Federated Learning (FL) is a promising privacy-aware distributed learning framework that can be deployed on various devices, such as mobile phones, desktops, and devices equipped with CPUs or GPUs. In the context of server-based Federated Learning as
Externí odkaz:
http://arxiv.org/abs/2408.08699
Federated learning (FL) is a distributed Machine Learning (ML) framework that is capable of training a new global model by aggregating clients' locally trained models without sharing users' original data. Federated learning as a service (FLaaS) offer
Externí odkaz:
http://arxiv.org/abs/2407.20573
Blockchain databases have attracted widespread attention but suffer from poor scalability due to underlying non-scalable blockchains. While blockchain sharding is necessary for a scalable blockchain database, it poses a new challenge named on-chain c
Externí odkaz:
http://arxiv.org/abs/2407.03750
Federated Learning (FL) is a decentralized machine learning approach where client devices train models locally and send them to a server that performs aggregation to generate a global model. FL is vulnerable to model inversion attacks, where the serv
Externí odkaz:
http://arxiv.org/abs/2405.01144
Artificial intelligence (AI) has immense potential in time series prediction, but most explainable tools have limited capabilities in providing a systematic understanding of important features over time. These tools typically rely on evaluating a sin
Externí odkaz:
http://arxiv.org/abs/2312.09513
Autor:
Chen, Shuaijun, Tavallaie, Omid, Hambali, Michael Henri, Zandavi, Seid Miad, Haddadi, Hamed, Lane, Nicholas, Guo, Song, Zomaya, Albert Y.
Federated learning (FL) is a novel distributed learning framework designed for applications with privacy-sensitive data. Without sharing data, FL trains local models on individual devices and constructs the global model on the server by performing mo
Externí odkaz:
http://arxiv.org/abs/2310.08147