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How Much Noise Is Enough: On Privacy, Security, and Accuracy Trade-Offs in Differentially Private Federated Learning
DescriptionCentralized machine learning techniques have caused privacy concerns for users. Federated Learning~(FL) mitigates this as a decentralized training system where no raw data are communicated across the network to a centralized server. Instead, the machine learning model is trained locally on each device and they send the locally-trained model weights to a central server to aggregate. However, there are critical challenges with FL. Security issues plague FL, such as model poisoning via label flipping. Additionally, there even exist privacy concerns via data leakage by reconstruction of weights. In this work, we apply differential privacy (which adds noise to the model weights before sending across the network) as an added privacy measure to protect sensitive data from being reconstructed. Through this research, we study the effects of differential privacy on FL with respect to security and privacy trade-offs.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
Posters
TimeTuesday, 14 November 202310am - 5pm MST
LocationDEF Concourse
TP
XO/EX