24 research outputs found
Crowd-ML: A Privacy-Preserving Learning Framework for a Crowd of Smart Devices
Smart devices with built-in sensors, computational capabilities, and network
connectivity have become increasingly pervasive. The crowds of smart devices
offer opportunities to collectively sense and perform computing tasks in an
unprecedented scale. This paper presents Crowd-ML, a privacy-preserving machine
learning framework for a crowd of smart devices, which can solve a wide range
of learning problems for crowdsensing data with differential privacy
guarantees. Crowd-ML endows a crowdsensing system with an ability to learn
classifiers or predictors online from crowdsensing data privately with minimal
computational overheads on devices and servers, suitable for a practical and
large-scale employment of the framework. We analyze the performance and the
scalability of Crowd-ML, and implement the system with off-the-shelf
smartphones as a proof of concept. We demonstrate the advantages of Crowd-ML
with real and simulated experiments under various conditions
Can Domain Adaptation Improve Accuracy and Fairness of Skin Lesion Classification?
Deep learning-based diagnostic system has demonstrated potential in
classifying skin cancer conditions when labeled training example are abundant.
However, skin lesion analysis often suffers from a scarcity of labeled data,
hindering the development of an accurate and reliable diagnostic system. In
this work, we leverage multiple skin lesion datasets and investigate the
feasibility of various unsupervised domain adaptation (UDA) methods in binary
and multi-class skin lesion classification. In particular, we assess three UDA
training schemes: single-, combined-, and multi-source. Our experiment results
show that UDA is effective in binary classification, with further improvement
being observed when imbalance is mitigated. In multi-class task, its
performance is less prominent, and imbalance problem again needs to be
addressed to achieve above-baseline accuracy. Through our quantitative
analysis, we find that the test error of multi-class tasks is strongly
correlated with label shift, and feature-level UDA methods have limitations
when handling imbalanced datasets. Finally, our study reveals that UDA can
effectively reduce bias against minority groups and promote fairness, even
without the explicit use of fairness-focused techniques