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Accelerating Fair Federated Learning: Adaptive Federated Adam
IEEE Transactions on Machine Learning in Communications and NetworkingFederated learning is a distributed and privacy-preserving approach to train a statistical model collaboratively from decentralized data held by different parties. However, when the datasets are not independent and identically distributed, models trained
Li Ju +3 more
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Dynamic Federated Learning [PDF]
2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2020 Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the ...
Rizk, E, Vlaski, S, Sayed, AH
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Blind Federated Edge Learning [PDF]
IEEE Transactions on Wireless Communications, 2021 submitted for publication.
Mohammad Mohammadi Amiri +4 more
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, 2020 How is it possible to allow multiple data owners to collaboratively train and use a shared prediction model while keeping all the local training data private Traditional machine learning approaches need to combine all data at one location, typically a data center, which may very well violate the laws on user privacy and data confidentiality.
Yang, Qiang ECE +5 more
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Communication-efficient federated learning [PDF]
Proceedings of the National Academy of Sciences, 2021 Significance Federated learning (FL) is an emerging paradigm that enables multiple devices to collaborate in training machine learning (ML) models without having to share their possibly private data. FL requires a multitude of devices to frequently exchange their learned model updates, thus introducing significant communication overhead ...
Chen, Mingzhe +4 more
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A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
Applied Sciences, 2023 The machine learning paradigms driven by the sixth-generation network (6G) facilitate an ultra-fast and low-latency communication environment. However, specific research and practical applications have revealed that there are still various issues ...
Yihang Luo +3 more
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Accelerating federated learning via momentum gradient descent [PDF]
, 2019 Federated learning (FL) provides a communication-efficient approach to solve machine learning problems concerning distributed data, without sending raw data to a central server. However, existing works on FL only utilize first-order gradient descent (GD)
Chen, Li +3 more
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JMIR Medical Informatics, 2021 BackgroundFederated learning is a decentralized approach to machine learning; it is a training strategy that overcomes medical data privacy regulations and generalizes deep learning algorithms.
Lee, Haeyun +15 more
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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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Robust federated learning with noisy communication [PDF]
, 2019 Federated learning is a communication-efficient training process that alternate between local training at the edge devices and averaging of the updated local model at the center server.
Ang, Fan +5 more
core +2 more sources