[month] [year]

Sambhav Solanki

Sambhav Solanki supervised by Dr. Sujit P Gujar received his Master of Science – Dual Degree  in Computer Science and Engineering (CSE). Here’s a summary of his research work on Optimizing Federated Agents For Fairness And Privacy In Bandits:

Federated learning offers a promising approach to learn collaboratively across multiple devices or agents. This collaboration allows leveraging a vast amount of data while preserving privacy by keeping the raw data local. However, this approach is not void of challenges. This thesis explores some of these challenges in the context of multi-armed bandit (MAB) problems. MAB problems are a type of decision-making problem where an agent repeatedly chooses between multiple actions, each with an unknown reward, and aims to maximize the total reward over time. Federated MABs introduce unique fairness concerns. For instances, for a fair algorithm, the learning process should not favor certain actions over others to an extreme degree, especially if those actions are beneficial to specific sub-populations. In addition to fairness, privacy is another key challenge. While federated learning avoids sharing raw data, the learning process itself can leak information about individual data points. This thesis proposes two novel algorithms (and variants) to address these challenges. The first algorithm, P-FCB, tackles a specific type of MAB problem with constraints in a federated setting. It promotes fairness by optimizing for the collective benefit of all users. The second algorithm, Fed-FairX-LinUCB, focuses on achieving fairness and privacy guarantees in a more complex scenario where actions have additional context. It ensures that action selection is fair and avoids situations where some actions are never chosen. Additionally, both these algorithms provide differential privacy guarantees, a formal guarantee that ensures the learning process reveals minimal information about any individual data point. By exploring these algorithms, this thesis aims to contribute to the development of federated learning for MAB problems that is both fair and privacy-preserving.

October 2024