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Found 8 papers, 2 papers with code
Communication-Efficient Federated Learning with Sketching
A key insight in the design of FedSketchedSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch.
Federated Learning Clients Clustering with Adaptation to Data Drifts
Clustered FL solutions address this by grouping clients with statistically similar data and training models for each cluster.
Amazon SageMaker Autopilot: a white box AutoML solution at scale
AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline.
Sketch and Scale: Geo-distributed tSNE and UMAP
Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security.
Practical and sample efficient zero-shot HPO
Zero-shot hyperparameter optimization (HPO) is a simple yet effective use of transfer learning for constructing a small list of hyperparameter (HP) configurations that complement each other.
FetchSGD: Communication-Efficient Federated Learning with Sketching
A key insight in the design of FetchSGD is that, because the Count Sketch is linear, momentum and error accumulation can both be carried out within the sketch.
Streaming Quantiles Algorithms with Small Space and Update Time
Approximating quantiles and distributions over streaming data has been studied for roughly two decades now.
Communication-efficient distributed SGD with Sketching
Large-scale distributed training of neural networks is often limited by network bandwidth, wherein the communication time overwhelms the local computation time.
