Results 11 to 20 of about 72,357 (237)
PLDP-FL: Federated Learning with Personalized Local Differential Privacy
Entropy, 2023 As a popular machine learning method, federated learning (FL) can effectively solve the issues of data silos and data privacy. However, traditional federated learning schemes cannot provide sufficient privacy protection.
Xiaoying Shen +4 more
semanticscholar +4 more sources
Federated Learning (FL) Model of Wind Power Prediction
IEEE AccessWind power is a cheap renewable energy that plays an important role in the economic development of a country. Identifying potential locations for energy production is challenging due to the diverse relationship between wind power potential and the ...
Amal Alshardan +4 more
semanticscholar +3 more sources
DESED-FL and URBAN-FL: Federated Learning Datasets for Sound Event Detection [PDF]
2021 29th European Signal Processing Conference (EUSIPCO), 2021 Research on sound event detection (SED) in environmental settings has seen increased attention in recent years. The large amounts of (private) domestic or urban audio data needed raise significant logistical and privacy concerns. The inherently distributed nature of these tasks, make federated learning (FL) a promising approach to take advantage of ...
Johnson, David S. +6 more
openaire +2 more sources
FL-Defender: Combating targeted attacks in federated learning
Knowledge-Based Systems, 2023 Federated learning (FL) enables learning a global machine learning model from local data distributed among a set of participating workers. This makes it possible i) to train more accurate models due to learning from rich joint training data, and ii) to improve privacy by not sharing the workers' local private data with others.
Najeeb Moharram Jebreel +1 more
openaire +2 more sources
Metaheuristics Algorithm-Based Minimization of Communication Costs in Federated Learning
IEEE Access, 2023 The Federated learning (FL) technique resolves the issue of training machine learning (ML) techniques on distributed networks, including the huge volume of modern smart devices.
Mohamed Ahmed Elfaki +7 more
doaj +1 more source
Stochastic Controlled Averaging for Federated Learning with Communication Compression [PDF]
International Conference on Learning Representations, 2023 Communication compression, a technique aiming to reduce the information volume to be transmitted over the air, has gained great interests in Federated Learning (FL) for the potential of alleviating its communication overhead.
Xinmeng Huang, Ping Li, Xiaoyun Li
semanticscholar +1 more source
SAFA : a semi-asynchronous protocol for fast federated learning with low overhead [PDF]
, 2020 Federated learning (FL) has attracted increasing attention as a promising approach to driving a vast number of end devices with artificial intelligence.
He, Ligang +5 more
core +2 more sources
Sensors, 2022 Deep learning-based Human Activity Recognition (HAR) systems received a lot of interest for health monitoring and activity tracking on wearable devices.
Gad Gad, Zubair Fadlullah
doaj +1 more source
Advances and Open Problems in Federated Learning [PDF]
Found. Trends Mach. Learn., 2019 Federated learning (FL) is a machine learning setting where many clients (e.g. mobile devices or whole organizations) collaboratively train a model under the orchestration of a central server (e.g.
P. Kairouz +57 more
semanticscholar +1 more source
New Generation Federated Learning
Sensors, 2022 With the development of the Internet of things (IoT), federated learning (FL) has received increasing attention as a distributed machine learning (ML) framework that does not require data exchange. However, current FL frameworks follow an idealized setup
Boyuan Li, Shengbo Chen, Zihao Peng
doaj +1 more source