Search Results for author:
Found 7 papers, 3 papers with code
Efficient Node Selection in Private Personalized Decentralized Learning
Personalized decentralized learning is a promising paradigm for distributed learning, enabling each node to train a local model on its own data and collaborate with other nodes to improve without sharing any data.
Decentralized Online Bandit Optimization on Directed Graphs with Regret Bounds
We consider a decentralized multiplayer game, played over $T$ rounds, with a leader-follower hierarchy described by a directed acyclic graph.
Adaptive Expert Models for Personalization in Federated Learning
Federated Learning (FL) is a promising framework for distributed learning when data is private and sensitive.
Federated learning using mixture of experts
In this paper, we propose a federated learning framework using a mixture of experts to balance the specialist nature of a locally trained model with the generalist knowledge of a global model in a federated learning setting.
Specialized federated learning using a mixture of experts
In federated learning, clients share a global model that has been trained on decentralized local client data.
Adversarial representation learning for synthetic replacement of private attributes
Data privacy is an increasingly important aspect of many real-world Data sources that contain sensitive information may have immense potential which could be unlocked using the right privacy enhancing transformations, but current methods often fail to produce convincing output.
Streaming word similarity mining on the cheap
Accurately and efficiently estimating word similarities from text is fundamental in natural language processing.
