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Current approaches to explainable AI (XAI) frequently fall short of providing genuinely understandable and trustworthy explanations. These methods often rely on post-hoc interpretations of black-box models, which can be misleading due to their failure to capture the underlying causal mechanisms driving predictions. Thi...
Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This...
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...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent data heterogeneity and potential biases present in these datasets pose significant challenges to achieving algorithmic fairness. This paper invest...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, traditional FL approaches often suffer from significant communication overhead and potential privacy vulnerabilities. This research proposes a nove...
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...
Federated learning (FL) offers a promising paradigm for training machine learning models on decentralized data sources without directly exchanging the data itself. However, traditional FL approaches often suffer from significant communication overhead and potential privacy vulnerabilities. This research proposes a nove...
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 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...
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...
Federated learning (FL) offers a promising approach to training machine learning models across decentralized devices while preserving data privacy. However, FL systems are vulnerable to Byzantine faults, where malicious participants introduce corrupted model updates, compromising the integrity of the global model. This...
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...
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