Results 31 to 40 of about 72,357 (237)
Research on federated learning approach based on local differential privacy
Tongxin xuebao, 2022 As a type of collaborative machine learning framework, federated learning is capable of preserving private data from participants while training the data into useful models.Nevertheless, from a viewpoint of information theory, it is still vulnerable for ...
Haiyan KANG, Yuanrui JI
doaj +2 more sources
Vertical Federated Learning:A Structured Literature Review [PDF]
, 2022 Federated Learning (FL) has emerged as a promising distributed learning paradigm with an added advantage of data privacy. With the growing interest in having collaboration among data owners, FL has gained significant attention of organizations.
Khan, Afsana +2 more
core +3 more sources
Continual Local Training for Better Initialization of Federated Models
, 2020 Federated learning (FL) refers to the learning paradigm that trains machine learning models directly in the decentralized systems consisting of smart edge devices without transmitting the raw data, which avoids the heavy communication costs and privacy ...
Sun, Lifeng, Yao, Xin
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Secure Smart Communication Efficiency in Federated Learning: Achievements and Challenges
Applied Sciences, 2022 Federated learning (FL) is known to perform machine learning tasks in a distributed manner. Over the years, this has become an emerging technology, especially with various data protection and privacy policies being imposed.
Seyedamin Pouriyeh +6 more
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Predictive intelligence to the edge through approximate collaborative context reasoning [PDF]
, 2017 We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event).
Anagnostopoulos, Christos +1 more
core +1 more source
K-FL: Kalman Filter-Based Clustering Federated Learning Method
IEEE Access, 2023 Federated learning is a distributed machine learning framework that enables a large number of devices to cooperatively train a model without data sharing. However, because federated learning trains a model using non-independent and identically distributed (non-IID) data stored at local devices, the weight divergence causes a performance loss.
Hyungbin Kim +4 more
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QUIC-FL: Quick Unbiased Compression for Federated Learning
, 2022 Distributed Mean Estimation (DME), in which $n$ clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-efficient federated learning. In this paper, we improve on previous DME techniques that achieve the optimal $O(1/n)$ Normalized Mean Squared Error (NMSE) guarantee by ...
Basat, Ran Ben +5 more
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Towards Efficient Federated Learning: Layer-Wise Pruning-Quantization Scheme and Coding Design
Entropy, 2023 As a promising distributed learning paradigm, federated learning (FL) faces the challenge of communication–computation bottlenecks in practical deployments. In this work, we mainly focus on the pruning, quantization, and coding of FL. By adopting a layer-
Zheqi Zhu +5 more
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6G White Paper on Machine Learning in Wireless Communication Networks
, 2020 The focus of this white paper is on machine learning (ML) in wireless communications. 6G wireless communication networks will be the backbone of the digital transformation of societies by providing ubiquitous, reliable, and near-instant wireless ...
Abbas, Robert +26 more
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Federated Learning for Internet of Things: A Comprehensive Survey [PDF]
IEEE Communications Surveys and Tutorials, 2021 The Internet of Things (IoT) is penetrating many facets of our daily life with the proliferation of intelligent services and applications empowered by artificial intelligence (AI).
Dinh C. Nguyen +5 more
semanticscholar +1 more source