12 citations · 25 across the 16 of their papers we have counts for
18 papers
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca +2
Federated Learning (FL) enables multiple clients to collaboratively train machine learning models while retaining data locality, thereby enhancing user privacy. However, traditiona…
Differentially Private Hierarchical Heavy Hitters
Ari Biswas, Graham Cormode, Yaron Kanza +2
The task of finding _Hierarchical_ Heavy Hitters (HHH) was introduced by Cormode et al. [VLDB 2003] as a generalisation of the heavy hitter problem. While finding HHH in data strea…
FedPS: Federated Preprocessing for structured data via aggregated Statistics
Xuefeng Xu, Graham Cormode
Federated Learning (FL) enables multiple parties to collaboratively train machine learning models without sharing raw data. However, before training, data must be preprocessed to a…
GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks
Samuel Maddock, Shripad Gade, Graham Cormode +1
State-of-the-art differentially private synthetic tabular data has been defined by adaptive 'select-measure-generate' frameworks, exemplified by methods like AIM. These approaches…
Federated Computation of ROC and PR Curves
Xuefeng Xu, Graham Cormode
Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curves are fundamental analytical tools for evaluating classification models, providing a comprehensive view of pe…
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…