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Federated learning (FL) presents a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, traditional FL methods often lack sufficient privacy guarantees, leaving users vulnerable to privacy breaches. This work proposes a novel framework for distribut...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting...
This paper proposes a novel distributed Bayesian optimization framework utilizing federated learning to optimize black-box functions across multiple clients while preserving data privacy. The core concept involves each client independently training a local Bayesian model based on its local dataset. Subsequently, a cent...
Federated learning (FL) offers a promising paradigm for training machine learning models across decentralized devices while preserving data privacy. However, recent research has demonstrated that FL systems are susceptible to adversarial attacks, where maliciously crafted inputs can significantly degrade model performa...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging the data itself. However, existing FL systems are susceptible to Byzantine attacks, where malicious participants can intentionally corrupt the learning process by submitting...
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...
Predictive maintenance aims to anticipate equipment failures, minimizing downtime and maintenance costs. Traditional approaches often rely on centralized data collection, raising significant privacy concerns, particularly when dealing with sensitive sensor data from industrial equipment. This research proposes a novel...
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 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...
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...
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...
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