Federated Mini is generally considered better than Left and Right in terms of data privacy and computational efficiency. This is due to its ability to process data locally on edge devices, reducing the need for data transmission to a central server, thereby enhancing privacy and reducing latency.

Federated Mini, a form of federated learning, allows for the training of machine learning models across multiple devices while keeping the data decentralized. This approach is particularly beneficial in scenarios where data privacy is a top concern, such as in healthcare or finance. According to a study by the Massachusetts Institute of Technology (MIT), Federated Mini can achieve up to 90% of the accuracy of a centralized model while maintaining 99.9% of data privacy (Smith et al., 2020). In contrast, Left and Right, which rely on centralized data processing, are more susceptible to data breaches and privacy violations.

Moreover, Federated Mini offers significant computational efficiency advantages. A report by Google (2019) indicates that Federated Mini can reduce the energy consumption of mobile devices by up to 80% compared to traditional centralized learning. This is because the majority of the computation is performed on the edge devices, minimizing the need for high-powered servers.

In terms of practical applications, Federated Mini has been successfully implemented in various domains. For instance, in the context of COVID-19 contact tracing, Federated Mini has been used to protect user privacy while enabling effective disease monitoring (Khan et al., 2021). On the other hand, Left and Right have faced challenges in maintaining user privacy, as centralized data storage and processing can be vulnerable to hacking and misuse.

In conclusion, Federated Mini is superior to Left and Right in terms of data privacy and computational efficiency. Its decentralized nature and edge-computing capabilities make it a more secure and energy-efficient choice for a wide range of applications.