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2,119 papers
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning pr...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
This paper presents a novel framework for distributed learning of Generative Adversarial Networks (GANs) through a federated learning approach. Traditional GAN training demands centralized computation and large-scale datasets, posing significant challenges for privacy-sensitive and resource-constrained environments. Th...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
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Decentralized Federated Learning (DFL) offers a promising approach to machine learning in resource-constrained environments like sensor networks, where data resides locally and communication is limited. However, traditional federated learning paradigms often fall short in providing robust privacy guarantees and are sus...
The Satellite Internet of Things (SIoT) leverages low Earth orbit (LEO) satellite constellations to support wide-area, real-time, and intelligent services such as disaster monitoring, ship tracking, and environmental sensing. These tasks demand collaborative model training across multiple satellites to improve predicti...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, achieving strong differential privacy (DP) guarantees in FL is a significant challenge, particularly when combined with compression techniques commonly employ...
Decentralized Federated Reinforcement Learning (DFRL) presents a promising approach to training robust and adaptable RL agents by leveraging distributed data and computational resources. However, this paradigm is susceptible to attacks from Byzantine actors who can inject malicious updates, jeopardizing the learning pr...
PNFR is designed, a privacy-preserving federated learning scheme for regressions training built on the MPaillier algorithm, which is three times faster than interactive federated regressions PrivFL and about 80% faster than non-interactive federated regressions VANE.
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
Amid the global transition toward carbon neutrality, digital technologies such as blockchain and federated learning offer a viable pathway to alleviating green financing constraints for small- and medium-sized enterprises (SMEs) and advancing supply chain decarbonization. Against this backdrop, this paper proposes a di...
This article presents a narrative review of Federated Learning and Privacy-Preserving AI in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpreti...
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
Education Innovation
Assistant Professor Benjamin Lindquist studies how engineers deliberately injected randomness into early AI, giving rise to deep-learning models that sometimes hallucinate.
A Blog post by NVIDIA on Hugging Face
A Blog post by IBM Research on Hugging Face
A new machine-learning approach broke records for weather forecasting for an important time frame in emergency planning.
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