Searched for: subject%3A%22federated%255C+learning%22
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Iacoban, Paula (author)
Federated Learning (FL) is a decentralized machine learning approach that provides a privacy-friendly way of training models by keeping the datasets of participating parties private. Some challenges FL faces are the lack of incentives to encourage participation in the learning process, as well as preventing potential cyber attacks that tamper...
master thesis 2024
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Wang, R. (author)
Federated Learning (FL) is a revolutionary approach to machine learning that enables collaborative model training among multiple parties without exposing sensitive data. Introduced by Google in 2016, FL taps into the wealth of data generated by edge devices while prioritizing user privacy and minimizing communication costs. Its applications span...
doctoral thesis 2024
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van Diepen, Chiel (author)
Indoor localisation is a well-researched topic and it is a challenge to improve the accuracy of existing techniques. In recent years, edge computing and federated learning have opened up new possibilities and challenges for indoor localisation. This thesis presents a federated implementation for spatial mapping of the network based on the RSSI...
master thesis 2024
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Raftopoulou, M. (author)
Following the trend of previous years, the number of devices, and hence the traffic in cellular networks is increasing. Moreover, new applications with stringent requirements are envisioned. Examples of such applications include collaborative learning and coverage extension with drones. To accommodate the traffic with its respective Quality of...
doctoral thesis 2024
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Van Opstal, Quinten (author)
Federated learning provides a lot of opportunities, especially with the built-in privacy considerations. There is however one attack that might compromise the utility of federated learning: backdoor attacks [14]. There are already some existing defenses, like flame [13] but they are computationally expensive [14]. This paper evaluates a version...
bachelor thesis 2024
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Nenovski, Lazar (author)
Abstract— Federated Learning (FL) makes it possible for a network of clients to jointly train a machine learning model, while also keeping the training data private. There are several approaches when designing a FL network and while most existing research is focused on a single-server design, new and promising variations are arising that make...
bachelor thesis 2024
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van der Meulen, Jan (author)
Federated learning (FL) is a privacy preserving machine learning approach which allows a machine learning model to be trained in a distributed fashion without ever sharing user data. Due to the large amount of valuable text and voice data stored on end-user devices, this approach works particularly well for natural language processing (NLP)...
bachelor thesis 2024
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Simonov, Alex (author)
Machine learning, a pivotal aspect of artificial intelligence, has dramatically altered our interaction with technology and our handling of extensive data. Through its ability to learn and make decisions from patterns and previous experiences, machine learning is growing in influence on different aspects of our lives. It is, however, shown that...
master thesis 2024
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Li, Martin (author)
In recent years, the rapid advancements in big data, machine learning, and artificial intelligence have led to a corresponding rise in privacy concerns. One of the solutions to address these concerns is federated learning. In this thesis, we will look at the setting of vertical federated learning based on tree models. We have built a system that...
master thesis 2024
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Llasag Rosero, Raúl (author), Silva, Catarina (author), Ribeiro, Bernardete (author), Santos, Bruno F. (author)
Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information....
journal article 2024
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Zhu, R. (author), Yang, M. (author), Wang, Q. (author)
Federated Learning (FL) has emerged as a privacy-preserving paradigm for collaborative deep learning model training across distributed data silos. Despite its importance, FL faces challenges such as high latency and less effective global models. In this paper, we propose ShuffleFL, an innovative framework stemming from the hierarchical FL, which...
journal article 2024
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Grataloup, Albin (author), Jonas, Stefan (author), Meyer, A. (author)
Federated learning has recently emerged as a privacy-preserving distributed machine learning approach. Federated learning enables collaborative training of multiple clients and entire fleets without sharing the involved training datasets. By preserving data privacy, federated learning has the potential to overcome the lack of data sharing in...
review 2024
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Zuo, Xiaojiang (author), Luopan, Yaxin (author), Han, Rui (author), Zhang, Qinglong (author), Liu, Chi Harold (author), Wang, Guoren (author), Chen, Lydia Y. (author)
Deep Neural Networks (DNNs) have been ubiquitously adopted in internet of things and are becoming an integral part of our daily life. When tackling the evolving learning tasks in real world, such as classifying different types of objects, DNNs face the challenge to continually retrain themselves according to the tasks on different edge...
journal article 2024
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Liu, TIANYI (author)
With the widespread application of artificial intelligence, centralized machine learning approaches, which require access to users' local data, have raised concerns about data privacy. In response, federated learning, an architecture that aggregates models trained locally with local data, has been proposed. This approach addresses the data...
master thesis 2023
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Rashad, Mohamed (author)
Autoencoders are popular neural networks that are able to compress high dimensional data to extract relevant latent information. TabNet is a state-of-the-art neural network model designed for tabular data that utilizes an autoencoder architecture for training. Vertical Federated Learning (VFL) is an emerging distributed machine learning paradigm...
master thesis 2023
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Mukherjee, Sayak (author)
Current methods in Federated and Decentralized learning presume that all clients share the same model architecture, assuming model homogeneity. However, in practice, this assumption may not always hold due to hardware differences. While prior research has addressed model heterogeneity in Federated Learning, it remains unexplored in fully...
master thesis 2023
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Zuo, Yuncong (author)
In federated learning systems, a server maintains a global model trained by a set of clients based on their local datasets. Conventional synchronous FL systems are very sensitive to system heterogeneity since the server needs to wait for the slowest clients in each round. Asynchronous fl partially addresses this bottleneck by dealing with...
master thesis 2023
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Chen, Congwen (author)
Current backdoor attacks against federated learning (FL) strongly rely on universal triggers or semantic patterns, which can be easily detected and filtered by certain defense mechanisms such as norm clipping, comparing parameter divergences among local updates. In this work, we propose a new stealthy and robust backdoor attack with flexible...
master thesis 2023
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Shankar, Aditya (author)
Vertical federated learning’s (VFL) immense potential for time series forecasting in industrial applications such as predictive maintenance and machine control remains untapped. Critical challenges to be addressed in the manufacturing industry include small and noisy datasets, model explainability, and stringent privacy requirements for training...
master thesis 2023
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Yu, Wenrui (author)
Privacy concerns in federated learning have attracted considerable attention recently. In centralized networks, it has been observed that even without directly exchanging raw training data, the exchange of other so-called intermediate parameters such as weights/gradients can still potentially reveal private information. However, there has been...
master thesis 2023
Searched for: subject%3A%22federated%255C+learning%22
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