Description: Joint Consensus is a method that allows multiple parties to reach an agreement within a federated learning framework. This approach is fundamental in environments where data is distributed across different devices or servers and where privacy and security are paramount. Unlike traditional machine learning methods that require centralizing data in one place, federated learning enables models to be trained locally on each device, using only the data available on that device. Joint Consensus is responsible for aggregating the results of these local trainings to create a global model that reflects the knowledge acquired from all participants. This process not only improves learning efficiency but also minimizes the risk of exposing sensitive data, as the data never leaves its original locations. The main features of Joint Consensus include its ability to handle data heterogeneity, its fault tolerance, and its scalability, making it a valuable tool in various applications, including healthcare, banking, and artificial intelligence. In summary, Joint Consensus is an essential component of federated learning, facilitating collaboration among multiple entities while protecting data privacy.
