MarS-FL: Enabling Competitors to Collaborate in Federated Learning

Learning When to Collaborate among Learning Agents

Multiagent systems o er a new paradigm where learning techniques can be useful. We focus on the application of lazy learning to multiagent systems where each agents learns individually and also learns when to cooperate in order to improve its performance. We show some experiments in which CBR agents use an adapted version of LID (Lazy Induction of Descriptions), a CBR method for classi cation. ...

Enabling Federated Search with Heterogeneous Search Engines

Learning to Collaborate from Delayed Rewards in Foraging Like Environments

Machine learning techniques are usually used in coordination problems and in competitive games but not in collaborative ones. Collaboration and coordination are different, while in coordination the task can not be concluded by a unique agent, in collaboration it can be solved by one agent or by a team, but the use of several agents has to be re ected in the performance of the system. In this wo...

Federated Multi-Task Learning

Federated learning poses new statistical and systems challenges in training machinelearning models over distributed networks of devices. In this work, we show thatmulti-task learning is naturally suited to handle the statistical challenges of thissetting, and propose a novel systems-aware optimization method, MOCHA, that isrobust to practical systems issues. Our method and theor...

willingness to communicate in the iranian context: language learning orientation and social support

why some learners are willing to communicate in english, concurrently others are not, has been an intensive investigation in l2 education. willingness to communicate (wtc) proposed as initiating to communicate while given a choice has recently played a crucial role in l2 learning. it was hypothesized that wtc would be associated with language learning orientations (llos) as well as social suppo...