Jack McFarland

Project

Fair RL-FL

Bias in Machine Learning (ML) can lead to unfair treatment of certain groups, particularly in areas like healthcare and finance, where disparate outcomes can have life-altering consequences. New training techniques aim to improve fairness while preserving privacy. Federated Learning (FL) is one such approach, allowing models to be trained on data from many devices without centralizing it. Instead of sharing raw data, each device trains a local model and sends model updates (adjustments based on its local data)to a central server, which aggregates them into a global model. This protects privacy while enabling large-scale training, but differences in data quality, representation, or access across devices can reinforce bias, leading to models that work well for some groups but poorly for others. This project tests whether a debiasing system can effectively mitigate bias in FL without sacrificing model performance. To tackle this, I’m adapting a Reinforcement Learning (RL) system, where an agent learns by interacting with an environment and receiving rewards for beneficial actions. The agent evaluates fairness using feedback from client devices and adjusts the central model’s weights before redistributing it for further training. Using fairness metrics and accuracy as its reward signal, the agent continuously refines its strategy, learning how to mitigate bias while preserving performance. Early results suggest this method can reduce bias while maintaining strong model accuracy, highlighting its potential for improving fairness in real world FL systems. If successful, this approach could be applied in areas like medical diagnostics, risk assessment in insurance, and hiring algorithms, where biased models can lead to significant real world harm.

What I learned

My project pushed me to work on my planning and problem solving skills in real time, particularly when my experiments didn’t run as expected and optimizations were difficult to achieve. Through many build iterations and experiment configurations I learned how to stay flexible under pressure while utilizing my technical skills. That experience of getting to test my skills under real world pressures was an invaluable learning opportunity for me which helped me grow not just as a researcher, but as a more confident and independent thinker.

Advice for future applicants

My advice to future Mary Gates Research applicants is to be open to failure and iteration, stay persistent, and don’t be afraid to ask questions or seek feedback. And I really mean that, we’re all here to support each other in improving the world. The people around you want you to succeed. Also, remember that communicating your work clearly is just as important as the work itself.

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