federated learning

Information-Theoretic Framework for Trustworthy Federated Learning

Federated Learning (FL) offers a promising paradigm for decentralized machine learning, enabling collaborative model training without direct data sharing. However, traditional privacy-preserving techniques within FL often rely on ad-hoc assumptions and lack a rigorous theoretical basis. This work introduces an informat...

Decentralized Federated Learning with Differential Privacy for Scientific Data

This paper presents a novel approach to collaborative scientific data analysis leveraging Decentralized Federated Learning with Differential Privacy (DFLDP). The core challenge in many scientific domains is the reluctance to share raw data due to stringent privacy regulations and intellectual property protections. Trad...

Distributed Knowledge Graph Embedding with Federated Learning for Privacy Preservation

Knowledge graph embedding techniques have gained significant traction in representing complex relationships within knowledge graphs, enabling applications such as link prediction, entity recommendation, and semantic search. However, the training of these embeddings often relies on consolidating vast amounts of data fro...

Federated Learning with Differential Privacy: Enhancing Privacy in Decentralized Model Training

Federated learning (FL) presents a promising paradigm for training machine learning models on decentralized data sources without directly exchanging data. However, the inherent collaborative nature of FL still poses privacy risks. This paper investigates the integration of differential privacy (DP) into the FL framewor...

**Decentralized Federated Learning with Byzantine Fault Tolerance using Graph Protocols**

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Distributed Bayesian Inference with Federated Learning and Differential Privacy

This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a frame...

Decentralized Learning with Federated Graph Neural Networks

This paper proposes a novel approach to decentralized learning utilizing Federated Graph Neural Networks (FedGNNs). The core idea is to facilitate collaborative knowledge discovery across a network of devices by employing locally maintained graph representations and periodically synchronized Graph Neural Networks (GNNs...

Machine Learning-Based Spectrum Forecasting for Ultra-Dense IoT Environments

The reach of Internet of Things (IoT) devices in cross domain applications has recently resulted in a significant rise in the burden on wireless communication systems. The modern wireless communication systems are under stress due to fast deployment of the Internet of Things’ (IoT) in homes, businesses, and smart citie...

Distributed Bayesian Inference with Federated Learning and Differential Privacy

This paper presents a novel approach to distributed Bayesian inference that leverages the strengths of federated learning and differential privacy. The core idea is to execute Bayesian inference locally on a network of devices, aggregating updates while simultaneously protecting individual privacy. We introduce a frame...

Decentralized Federated Reinforcement Learning with Multi-Agent Credit Assignment

This paper presents a novel approach to decentralized federated reinforcement learning (DFRL) that tackles the critical challenge of multi-agent credit assignment. Traditional multi-agent reinforcement learning (MARL) struggles to scale effectively in decentralized settings due to the difficulty in determining which ag...

Machine Learning - Based Classification Framework for Intelligent 5G Resource Allocation and Network Optimization

Communication-Efficient Federated Frank-Wolfe Algorithm