in the system. Note that all these works rely on a trusted central server to coor-dinate the distributed learning process, which obviously becomes a single point of failure and can be subject to attacks. Only a couple of works like31 attempt to design decentralized multiparty learning systems. Note that only linear models are considered in31. To the best of our knowledge, secure decentralized multiparty learning with heterogeneous models remains an open and challenging problem.In this section, we propose a novel secure decentralized multiparty learning sys-tem by taking advantage of the…mehr
in the system. Note that all these works rely on a trusted central server to coor-dinate the distributed learning process, which obviously becomes a single point of failure and can be subject to attacks. Only a couple of works like31 attempt to design decentralized multiparty learning systems. Note that only linear models are considered in31. To the best of our knowledge, secure decentralized multiparty learning with heterogeneous models remains an open and challenging problem.In this section, we propose a novel secure decentralized multiparty learning sys-tem by taking advantage of the blockchain technology, called BEMA. In particular, each party in a decentralized system broadcasts its local model, and meanwhile, processes the received (heard) models from other parties over his local dataset, and identifies the models that need to be calibrated. Following our designed pro-tocol, the party sends the calibration message to the corresponding parties. In so doing, the parties in the system do not need to share their whole dataset with other parties. In this system, we consider two types of Byzantine attacks in the system, which can occur in model broadcasting and model calibration processes. To pro-tect system security, we carefully design "o -chain sample mining" and "on-chain
Dr. Naviya is a leading expert in the field of machine learning, with a distinguished career dedicated to unlocking the full potential of multiparty learning algorithms. Her particular focus lies in addressing a critical challenge: heterogeneity, the presence of significant variations in data used to train these algorithms. "Bridging the Gap: Addressing Heterogeneity in Local Models for Enhanced Multiparty Learning" represents Dr. Naviya's culmination of years spent researching and developing innovative solutions to overcome the limitations of traditional multiparty learning models. Dr. Naviya meticulously analyzes how data heterogeneity can lead to inaccurate predictions and suboptimal performance. Dr. Naviya's passion extends beyond theoretical solutions. They are a strong advocate for developing practical methods that can be readily implemented in real-world applications. Dr. Naviya actively collaborates with researchers and engineers to design new algorithms and frameworks that account for data heterogeneity and enable robust multiparty learning across diverse datasets. Their writing is known for its clarity and depth, effectively bridging the gap between complex machine learning concepts and practical considerations for data scientists and engineers. In "Bridging the Gap," Dr. Naviya embarks on a thought-provoking exploration of heterogeneity in multiparty learning. They delve into the technical challenges posed by data variations, showcase cutting-edge solutions that leverage the power of diverse data sources, and explore the transformative impact these advancements will have on various fields that rely on multiparty learning, such as healthcare, finance, and autonomous systems. Dr. Naviya's insightful analysis equips readers to understand the importance of addressing heterogeneity and empowers them to develop more robust and effective multiparty learning models.
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