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Found 13 papers, 1 papers with code
GPU-based Private Information Retrieval for On-Device Machine Learning Inference
Together, for various on-device ML applications such as recommendation and language modeling, our system on a single V100 GPU can serve up to $100, 000$ queries per second -- a $>100 \times$ throughput improvement over a CPU-based baseline -- while maintaining model accuracy.
Enhancing Stratospheric Weather Analyses and Forecasts by Deploying Sensors from a Weather Balloon
We propose a framework to collect stratospheric data by releasing a contrail of tiny sensor devices as a weather balloon ascends.
CPR: Understanding and Improving Failure Tolerant Training for Deep Learning Recommendation with Partial Recovery
The paper is the first to the extent of our knowledge to perform a data-driven, in-depth analysis of applying partial recovery to recommendation models and identified a trade-off between accuracy and performance.
Sustainable AI: Environmental Implications, Challenges and Opportunities
This paper explores the environmental impact of the super-linear growth trends for AI from a holistic perspective, spanning Data, Algorithms, and System Hardware.
Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity
Federated learning (FL) is an effective mechanism for data privacy in recommender systems by running machine learning model training on-device.
FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learning
Despite FL's initial success, many important deep learning use cases, such as ranking and recommendation tasks, have been limited from on-device learning.
Cocktail Party Attack: Breaking Aggregation-Based Privacy in Federated Learning using Independent Component Analysis
Federated learning (FL) aims to perform privacy-preserving machine learning on distributed data held by multiple data owners.
Measuring and Controlling Split Layer Privacy Leakage Using Fisher Information
Split learning and inference propose to run training/inference of a large model that is split across client devices and the cloud.
Data Leakage via Access Patterns of Sparse Features in Deep Learning-based Recommendation Systems
This paper explores the private information that may be learned by tracking a recommendation model's sparse feature access patterns.
Green Federated Learning
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets.
Information Flow Control in Machine Learning through Modular Model Architecture
In today's machine learning (ML) models, any part of the training data can affect its output.
Approximating ReLU on a Reduced Ring for Efficient MPC-based Private Inference
Secure multi-party computation (MPC) allows users to offload machine learning inference on untrusted servers without having to share their privacy-sensitive data.
LazyDP: Co-Designing Algorithm-Software for Scalable Training of Differentially Private Recommendation Models
Differential privacy (DP) is widely being employed in the industry as a practical standard for privacy protection.
