Code reviews are essential for software quality, but they're time-consuming. Imagine if AI could automate this process, freeing up developers to focus on building innovative features. New research explores how federated learning and large language models (LLMs) can transform code review automation, potentially revolutionizing software development workflows. Researchers at the Indian Institute of Technology Tirupati are tackling the challenge of applying LLMs to private codebases, where sharing sensitive data is a major concern. Their study explores "federated learning," a privacy-preserving technique that allows multiple organizations to collaboratively train a shared LLM without directly exchanging their data. Each participant trains the LLM locally on its own code, then shares only the learned model updates with a central server. This allows the LLM to learn from a much larger and more diverse dataset than any single organization could provide, resulting in a more robust and generalizable code review tool. The research investigates three key aspects of automated code review: predicting whether a code change needs review, generating insightful comments, and even suggesting code refinements. The team experimented with different multi-task training strategies, finding that while sequential training led to the model forgetting previously learned tasks, a cumulative approach showed promising results. This means the AI could potentially handle all three aspects of code review within a single unified model. The implications are significant. This technology could dramatically reduce the time and effort spent on code reviews, allowing developers to focus on more creative work. Moreover, the federated learning approach ensures data privacy, making it feasible for companies to collaborate on building better AI tools without compromising their sensitive code. While still in its early stages, this research paves the way for a future where AI plays a central role in ensuring code quality and accelerating software development. Future research will focus on improving multi-task learning strategies, perhaps by exploring techniques from continual learning to minimize the forgetting problem and further enhance the AI's ability to provide comprehensive and accurate code review feedback.