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2,117 papers
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources, preserving data privacy. However, the inherent data heterogeneity across clients introduces a significant challenge: algorithmic bias. This paper presents a novel method for detecting and quantifying b...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly sharing the data itself. However, existing FL systems face significant vulnerabilities, including malicious participant attacks and data breaches. This paper proposes a novel decentrali...
Federated learning (FL) has emerged as a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the raw data. However, the application of FL to graph analytics, particularly when dealing with sensitive graph data, presents significant challenges due to the inhe...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL models often operate under the assumption of homogeneous data distributions, which frequently doesn't hold true in real-world scenarios. This researc...
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Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, traditional differential privacy (DP) techniques, designed for centralized learning, often lead to substantial accuracy degradation in FL settings due to the i...
Federated learning (FL) offers a promising approach to train machine learning models on decentralized data sources without directly exchanging the data itself. However, the inherent privacy risks associated with FL remain a significant concern. Traditional differential privacy (DP) techniques often struggle to effectiv...
Start learning: Bahasa Indonesia · English interface. Version 0.63.23 applies reciprocal learner navigation across the complete hosted program surface: every reader links to its course and localized program, every course links to its readers, and hosted copies retain prominent authoritative-original routes. Current lan...
Federated learning offers a compelling architecture for peacebuilding analytics in conflict-affected regions, where data sovereignty, institutional sensitivity, and infrastructural fragility preclude centralised data aggregation. We introduce a federated empirical risk minimisation objective regularised by a Wasserstei...
Reliable marine-engine fault diagnosis in maritime IoT is challenged by distributed data ownership, heterogeneous fault distributions, and continuously changing operating conditions. This paper proposes SeaCausal-FL, a federated fuzzy causal learning framework that combines a shared temporal diagnostic path with mechan...
Start learning: Bahasa Indonesia · English interface. Version 0.63.22 applies reciprocal learner navigation across the complete hosted program surface: every reader links to its course and localized program, every course links to its readers, and hosted copies retain prominent authoritative-original routes. Current lan...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, a key challenge in the widespread adoption of FL is the incentive problem: motivating participants to contribute their valuable data and computational resource...
This paper proposes a novel decentralized federated learning (DFL) system designed to address the critical challenges of privacy and accountability inherent in collaborative learning environments. The system leverages the strengths of three key technologies: decentralized learning, differential privacy, and blockchain...
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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