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Found 5 papers, 2 papers with code
Network Adaptive Federated Learning: Congestion and Lossy Compression
In order to achieve the dual goals of privacy and learning across distributed data, Federated Learning (FL) systems rely on frequent exchanges of large files (model updates) between a set of clients and the server.
Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints
The proposed algorithm is tested in a variety of settings for intermittently available clients under communication constraints, and its efficacy demonstrated on synthetic data and realistically federated benchmarking experiments using CIFAR100 and Shakespeare datasets.
Book-Ahead & Supply Management for Ridesourcing Platforms
Second, given the admission control policy and reservations information in each region, we predict the ``target" number of drivers that is required (in the future) to probabilistically guarantee the reach time service requirement for stochastic non-reserved rides.
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling
The latter implies that uniform sampling strategies with a fixed sampling size achieve a non-trivial approximation factor; however, we show that with overwhelming probability, these methods fail to find the optimal subset.
Modeling and Optimization of Human-machine Interaction Processes via the Maximum Entropy Principle
We propose a data-driven framework to enable the modeling and optimization of human-machine interaction processes, e. g., systems aimed at assisting humans in decision-making or learning, work-load allocation, and interactive advertising.
