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Research review of federated learning algorithms
大数据, 2020 In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related
Jianzong WANG +6 more
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A Review of Research on Secure Aggregation for Federated Learning
Future InternetFederated learning (FL) is an advanced distributed machine learning method that effectively solves the data silo problem. With the increasing popularity of federated learning and the growing importance of privacy protection, federated learning methods ...
Xing Zhang, Yuexiang Luo, Tianning Li
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PeFLL: Personalized Federated Learning by Learning to Learn
, 2023 We present PeFLL, a new personalized federated learning algorithm that improves over the state-of-the-art in three aspects: 1) it produces more accurate models, especially in the low-data regime, and not only for clients present during its training phase, but also for any that may emerge in the future; 2) it reduces the amount of on-client computation ...
Scott, Jonathan A +2 more
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A Review of Federated Learning in Agriculture
Sensors, 2023 Federated learning (FL), with the aim of training machine learning models using data and computational resources on edge devices without sharing raw local data, is essential for improving agricultural management and smart agriculture. This study is a review of FL applications that address various agricultural problems.
Krista Rizman Zalik, Mitja Zalik
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Blockchained On-Device Federated Learning [PDF]
IEEE Communications Letters, 2020 to appear in IEEE Communications ...
Hyesung Kim +3 more
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Benchmark for Personalized Federated Learning
IEEE Open Journal of the Computer SocietyFederated learning is a distributed machine learning approach that allows a single server to collaboratively build machine learning models with multiple clients without sharing datasets.
Koji Matsuda +3 more
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Personalized Federated Learning With a Graph
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Knowledge sharing and model personalization are two key components in the conceptual framework of personalized federated learning (PFL). Existing PFL methods focus on proposing new model personalization mechanisms while simply implementing knowledge sharing by aggregating models from all clients, regardless of their relation graph.
Fengwen Chen +4 more
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EURASIP Journal on Audio, Speech, and Music Processing, 2023 Speaker recognition, the process of automatically identifying a speaker based on individual characteristics in speech signals, presents significant challenges when addressing heterogeneous-domain conditions.
Zhiyong Chen, Shugong Xu
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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
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Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks. In this study, our focus is on backdoor attacks in which the adversary's goal is to cause targeted misclassifications for inputs embedded with an adversarial trigger while maintaining an acceptable ...
Omid Aramoon +3 more
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