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Found 11 papers, 3 papers with code
Hyperparameter Optimization for SecureBoost via Constrained Multi-Objective Federated Learning
This vulnerability may lead the current heuristic hyperparameter configuration of SecureBoost to a suboptimal trade-off between utility, privacy, and efficiency, which are pivotal elements toward a trustworthy federated learning system.
HeteFedRec: Federated Recommender Systems with Model Heterogeneity
Owing to the nature of privacy protection, federated recommender systems (FedRecs) have garnered increasing interest in the realm of on-device recommender systems.
SecureBoost Hyperparameter Tuning via Multi-Objective Federated Learning
To fill this gap, we propose a Constrained Multi-Objective SecureBoost (CMOSB) algorithm to find Pareto optimal solutions that each solution is a set of hyperparameters achieving optimal tradeoff between utility loss, training cost, and privacy leakage.
A Data-driven Region Generation Framework for Spatiotemporal Transportation Service Management
In this paper, we propose RegionGen, a data-driven region generation framework that can specify regions with key characteristics (e. g., good spatial semantic meaning and predictability) by modeling region generation as a multi-objective optimization problem.
p-Meta: Towards On-device Deep Model Adaptation
Data collected by IoT devices are often private and have a large diversity across users.
Learning to Assign: Towards Fair Task Assignment in Large-Scale Ride Hailing
Ride hailing is a widespread shared mobility application where the central issue is to assign taxi requests to drivers with various objectives.
Transferring Knowledge Distillation for Multilingual Social Event Detection
Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce.
An Efficient Approach for Cross-Silo Federated Learning to Rank
Traditional learning-to-rank (LTR) models are usually trained in a centralized approach based upon a large amount of data.
Value Function is All You Need: A Unified Learning Framework for Ride Hailing Platforms
In this paper we propose a unified value-based dynamic learning framework (V1D3) for tackling both tasks.
Pruning-Aware Merging for Efficient Multitask Inference
Given a set of deep neural networks, each pre-trained for a single task, it is desired that executing arbitrary combinations of tasks yields minimal computation cost.
Federated Machine Learning: Concept and Applications
We propose a possible solution to these challenges: secure federated learning.
