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This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual data...
This paper proposes a novel approach to decentralized federated learning (DFL) that incorporates Byzantine fault tolerance (BFT) using blockchain consensus. Traditional DFL systems are vulnerable to malicious clients injecting poisoned model updates, compromising the overall model accuracy and data integrity. Our syste...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, the inherent non-IID (independent and identically distributed) nature of data across clients poses a significant threat to the privacy guarantees provided by t...
This paper proposes a novel framework for distributed causal inference by integrating federated learning with Bayesian networks. Traditional causal inference methods often require centralized data access, posing significant privacy concerns and logistical challenges, particularly in scenarios involving sensitive data d...
Federated learning (FL) presents a promising approach to training machine learning models on decentralized data sources while preserving user privacy. However, traditional FL methods often rely on a central server, introducing a single point of failure and potential privacy risks. This paper proposes a novel decentrali...
The creation and maintenance of digital twins for critical infrastructure systems, such as power grids, water distribution networks, and transportation systems, presents significant computational challenges. Traditional centralized approaches struggle to handle the massive amounts of data generated by distributed senso...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...
This paper presents a novel approach to differential privacy in federated learning, termed Dynamic Differential Privacy (DDP). The core idea is to adapt the privacy budget dynamically, responding to the varying sensitivity of local data. Traditional federated learning frequently utilizes a static privacy budget, which...
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without direct data sharing. However, traditional FL methods are vulnerable to information leakage and model divergence, particularly when dealing with highly heterogeneous client datasets. This researc...
This paper proposes a novel approach to decentralized federated learning (DFL) that leverages secure multi-party computation (SMPC) to guarantee data privacy during collaborative model training. Traditional federated learning methods, while promoting data sharing for model improvement, inherently expose individual data...
This paper proposes a novel approach to decentralized federated learning (DFL) that incorporates Byzantine fault tolerance (BFT) using blockchain consensus. Traditional DFL systems are vulnerable to malicious clients injecting poisoned model updates, compromising the overall model accuracy and data integrity. Our syste...
Federated Learning (FL) offers a promising approach to training machine learning models on decentralized data while preserving data privacy. However, the inherent heterogeneity of data across clients and potential malicious behavior introduce challenges to the trustworthiness of FL systems. This work proposes a novel i...
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