- Lingjuan Lyu
- Chen Chen
A Novel Attribute Reconstruction Attack in Federated Learning
Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of partici- pants to construct a joint ML model without expos- ing their private training data. Existing FL designs have been shown to exhibit vulnerabilities which can be exploited by adversaries both within and outside of the system to compromise data privacy. However, most current works conduct attacks by leveraging gradients on a small batch of data, which is less practical in FL. In this work, we consider a more practical and interesting scenario in which partici- pants share their epoch-averaged gradients (share gradients after at least 1 epoch of local training) rather than per-example or small batch-averaged gradients as in previous works. We perform the first systematic evaluation of attribute reconstruction at- tack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared epoch-averaged local model gradients can reveal sensitive attributes of local training data of any victim participant. To achieve this goal, we de- velop a more effective and efficient gradient match- ing based method called cos-matching to reconstruct the training data attributes. We evaluate our attacks on a variety of real-world datasets, scenarios, as- sumptions. Our experiments show that our proposed method achieves better attribute attack performance than most existing baselines.
MocoSFL: enabling cross-client collaborative self-supervised learning
Existing collaborative self-supervised learning (SSL) schemes are not suitable for cross-client applications because of their expensive computation and large local data requirements. To address these issues, we propose MocoSFL, a collaborative SSL framework based on Split Fe…
IDEAL: Query-Eﬀicient Data-Free Learning from Black-Box Models
Knowledge Distillation (KD) is a typical method for training a lightweight student model with the help of a well-trained teacher model. However, most KD methods require access to either the teacher's training data or model parameter, which is unrealistic. To tackle this prob…
Twofer: Tackling Continual Domain Shift with Simultaneous Domain Generalization and Adaptation
In real-world applications, deep learning models often run in non-stationary environments where the target data distribution continually shifts over time. There have been numerous domain adaptation (DA) methods in both online and offline modes to improve cross-domain adaptat…
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.