Search Results for author:
Found 16 papers, 4 papers with code
Noise Masking Attacks and Defenses for Pretrained Speech Models
Our method fine-tunes the encoder to produce an ASR model, and then performs noise masking on this model, which we find recovers private information from the pretraining data, despite the model never having seen transcripts at pretraining time!
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
RareBench: Can LLMs Serve as Rare Diseases Specialists?
Meanwhile, we have compiled the largest open-source dataset on rare disease patients, establishing a benchmark for future studies in this domain.
MergeSFL: Split Federated Learning with Feature Merging and Batch Size Regulation
Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine valuable knowledge in edge computing (EC) systems.
Unintended Memorization in Large ASR Models, and How to Mitigate It
We empirically show that clipping each example's gradient can mitigate memorization for sped-up training examples with up to 16 repetitions in the training set.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Why Is Public Pretraining Necessary for Private Model Training?
To explain this phenomenon, we hypothesize that the non-convex loss landscape of a model training necessitates an optimization algorithm to go through two phases.
Byzantine-Robust Federated Learning with Optimal Statistical Rates and Privacy Guarantees
In contrast to prior work, our proposed protocols improve the dimension dependence and achieve a tight statistical rate in terms of all the parameters for strongly convex losses.
Secure Byzantine-Robust Federated Learning with Dimension-free Error
In the present work, we propose a federated learning protocol with bi-directional security guarantees.
FED-$\chi^2$: Secure Federated Correlation Test
In this paper, we propose the first secure federated $\chi^2$-test protocol, FED-$\chi^2$.
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning
Recent research has confirmed the feasibility of backdoor attacks in deep reinforcement learning (RL) systems.
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Deep learning techniques have achieved remarkable performance in wide-ranging tasks.
F^2ed-Learning: Good Fences Make Good Neighbors
In this paper, we present F^2ed-Learning, the first federated learning protocol simultaneously defending against both semi-honest server and Byzantine malicious clients.
D2p-fed:Differentially Private Federated Learning with Efficient Communication
We provide complete analysis of the privacy guarantee, communication cost and convergence rate of D2p-fed.
Towards Bidirectional Protection in Federated Learning
At one end of the spectrum, some work uses secure aggregation techniques to hide the individual client's updates and only reveal the aggregated global update to a malicious server that strives to infer the clients' privacy from their updates.
D2P-Fed: Differentially Private Federated Learning With Efficient Communication
In this paper, we propose the discrete Gaussian based differentially private federated learning (D2P-Fed), a unified scheme to achieve both differential privacy (DP) and communication efficiency in federated learning (FL).
Towards practical differentially private causal graph discovery
Causal graph discovery refers to the process of discovering causal relation graphs from purely observational data.
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI Systems
As such, given a deep neural network model and clean input samples, it is very challenging to inspect and determine the existence of a trojan backdoor.
