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FraudTransformer: Time-Aware GPT for Transaction Fraud Detection
Gholamali Aminian, Andrew Elliott, Tiger Li +8
Detecting payment fraud in real-world banking streams requires models that can exploit both the order of events and the irregular time gaps between them. We introduce FraudTransfor…
Fairness-Constrained Optimization Attack in Federated Learning
Harsh Kasyap, Minghong Fang, Zhuqing Liu +2
Federated learning (FL) is a privacy-preserving machine learning technique that facilitates collaboration among participants across demographics. FL enables model sharing, while re…
Private Federated Multiclass Post-hoc Calibration
Samuel Maddock, Graham Cormode, Carsten Maple
Calibrating machine learning models so that predicted probabilities better reflect the true outcome frequencies is crucial for reliable decision-making across many applications. In…
Vision Transformer with Adversarial Indicator Token against Adversarial Attacks in Radio Signal Classifications
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +4
The remarkable success of transformers across various fields such as natural language processing and computer vision has paved the way for their applications in automatic modulatio…
Federated and differentially private estimation of KL divergence
Mary Scott, Sayan Biswas, Graham Cormode +1
Measuring distribution drifts is a key task in managing distributed, sensitive data, as it underpins a wide range of federated learning and analytics applications. In many practica…
Towards Robust Federated Analytics via Differentially Private Measurements of Statistical Heterogeneity
Mary Scott, Graham Cormode, Carsten Maple
Statistical heterogeneity is a measure of how skewed the samples of a dataset are. It is a common problem in the study of differential privacy that the usage of a statistically het…