Fair Differentially Private Federated Learning Framework
Ayush K. Varshney Sonakshi Garg Arka Ghosh Sargam Gupta
Federated learning (FL) is a distributed machine learning strategy that
enables participants to collaborate and train a shared model without sharing
their individual datasets. Privacy and fairness are crucial considerations in
FL. While FL promotes privacy by minimizing the amount of user data stored on
central servers, it still poses privacy risks that need to be addressed.
Industry standards such as differential privacy, secure multi-party
computation, homomorphic encryption, and secure aggregation protocols are
followed to ensure privacy in FL. Fairness is also a critical issue in FL, as
models can inherit biases present in local datasets, leading to unfair
predictions. Balancing privacy and fairness in FL is a challenge, as privacy
requires protecting user data while fairness requires representative training
data. This paper presents a "Fair Differentially Private Federated Learning
Framework" that addresses the challenges of generating a fair global model
without validation data and creating a globally private differential model. The
framework employs clipping techniques for biased model updates and Gaussian
mechanisms for differential privacy. The paper also reviews related works on
privacy and fairness in FL, highlighting recent advancements and approaches to
mitigate bias and ensure privacy. Achieving privacy and fairness in FL requires
careful consideration of specific contexts and requirements, taking into
account the latest developments in industry standards and techniques.