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Found 8 papers, 3 papers with code
Aligning Large Language Models by On-Policy Self-Judgment
Existing approaches for aligning large language models with human preferences face a trade-off that requires a separate reward model (RM) for on-policy learning.
Green Federated Learning
The rapid progress of AI is fueled by increasingly large and computationally intensive machine learning models and datasets.
Reconciling Security and Communication Efficiency in Federated Learning
Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees.
Papaya: Practical, Private, and Scalable Federated Learning
Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients.
Opacus: User-Friendly Differential Privacy Library in PyTorch
We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus. ai).
Federated Learning with Buffered Asynchronous Aggregation
On the other hand, asynchronous aggregation of client updates in FL (i. e., asynchronous FL) alleviates the scalability issue.
ResiliNet: Failure-Resilient Inference in Distributed Neural Networks
Nevertheless, when a neural network is partitioned and distributed among physical nodes, failure of physical nodes causes the failure of the neural units that are placed on those nodes, which results in a significant performance drop.
Guardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud
To articulate deepFogGuard, we introduce the elements and a model for the resiliency of distributed DNN inference.
