3 accepted papers
Motivated by high resource costs of centralized machine learning schemes as well as data privacy concerns, federated learning (FL) emerged as an efficient alternative that relies on aggregating locally trained models rather than collecting clients' potentially private data. In practice, available re…
We study non-convex optimization problems where the data is distributed across nodes of a time-varying directed network; this describes dynamic settings in which the communication between network nodes is affected by delays or link failures. The network nodes, which can access only their local objec…
We consider the problem of decentralized optimization where a collection of agents, each having access to a local cost function, communicate over a time-varying directed network and aim to minimize the sum of those functions. In practice, the amount of information that can be exchanged between the a…