Results 11 to 20 of about 4,684 (203)
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 ...
David S. Johnson 0003 +6 more
openaire +2 more sources
WW-FL: Secure and Private Large-Scale Federated Learning
Transactions on Cryptographic Hardware and Embedded SystemsFederated learning (FL) is an efficient approach for large-scale distributed machine learning that promises data privacy by keeping training data on client devices.
Felix Marx +5 more
doaj +3 more sources
FL Games: A federated learning framework for distribution shifts
CoRR, 2022 Accepted as ORAL at NeurIPS Workshop on Federated Learning: Recent Advances and New Challenges.
Sharut Gupta +4 more
openaire +3 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
FL-Market: Trading Private Models in Federated Learning
2022 IEEE International Conference on Big Data (Big Data), 2022 The difficulty in acquiring a sufficient amount of training data is a major bottleneck for machine learning (ML) based data analytics. Recently, commoditizing ML models has been proposed as an economical and moderate solution to ML-oriented data acquisition.
Shuyuan Zheng +4 more
openaire +2 more sources
QUIC-FL: Quick Unbiased Compression for Federated Learning
CoRR, 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 ...
Ran Ben Basat +5 more
openaire +2 more sources
Adaptive secure malware efficient machine learning algorithm for healthcare data
CAAI Transactions on Intelligence Technology, EarlyView., 2023 Abstract Malware software now encrypts the data of Internet of Things (IoT) enabled fog nodes, preventing the victim from accessing it unless they pay a ransom to the attacker. The ransom injunction is constantly accompanied by a deadline. These days, ransomware attacks are too common on IoT healthcare devices.
Mazin Abed Mohammed +8 more
wiley +1 more source
Periodontal diseases and adverse pregnancy outcomes. Present and future
Periodontology 2000, EarlyView., 2023 Abstract For more than two decades the possible association between periodontal diseases and adverse pregnancy outcomes has been extensively evaluated. Numerous observational, intervention, and mechanistic studies have offered valuable information on this topic.
Yiorgos A. Bobetsis +3 more
wiley +1 more source
IP-FL: Incentivized and Personalized Federated Learning
, 2023 Existing incentive solutions for traditional Federated Learning (FL) focus on individual contributions to a single global objective, neglecting the nuances of clustered personalization with multiple cluster-level models and the non-monetary incentives such as personalized model appeal for clients.
Khan, Ahmad Faraz +9 more
openaire +2 more sources
PersA-FL : personalized asynchronous federated learning
Optimization Methods and SoftwareWe study the personalized federated learning problem under asynchronous updates. In this problem, each client seeks to obtain a personalized model that simultaneously outperforms local and global models. We consider two optimization-based frameworks for personalization: (i) Model-Agnostic Meta-Learning (MAML) and (ii) Moreau Envelope (ME).
Mohammad Taha Toghani +2 more
openaire +2 more sources