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Found 6 papers, 1 papers with code
FLIPS: Federated Learning using Intelligent Participant Selection
In particular, we examine the benefits of label distribution clustering on participant selection in federated learning.
Separation of Powers in Federated Learning
In this paper, we introduce TRUDA, a new cross-silo FL system, employing a trustworthy and decentralized aggregation architecture to break down information concentration with regard to a single aggregator.
MYSTIKO : : Cloud-Mediated, Private, Federated Gradient Descent
Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients.
Effective Elastic Scaling of Deep Learning Workloads
We design a fast dynamic programming based optimizer to solve this problem in real-time to determine jobs that can be scaled up/down, and use this optimizer in an autoscaler to dynamically change the allocated resources and batch sizes of individual DL jobs.
FfDL : A Flexible Multi-tenant Deep Learning Platform
This paper describes the design, implementation and our experiences with FfDL, a DL platform used at IBM.
IBM Deep Learning Service
Deep learning driven by large neural network models is overtaking traditional machine learning methods for understanding unstructured and perceptual data domains such as speech, text, and vision.
Distributed, Parallel, and Cluster Computing
