  Federated learning has become increasingly important for modern machine
learning, especially for data privacy-sensitive scenarios. Existing federated
learning mostly adopts the central server-based architecture or centralized
architecture. However, in many social network scenarios, centralized federated
learning is not applicable (e.g., a central agent or server connecting all
users may not exist, or the communication cost to the central server is not
affordable). In this paper, we consider a generic setting: 1) the central
server may not exist, and 2) the social network is unidirectional or of
single-sided trust (i.e., user A trusts user B but user B may not trust user
A). We propose a central server free federated learning algorithm, named Online
Push-Sum (OPS) method, to handle this challenging but generic scenario. A
rigorous regret analysis is also provided, which shows very interesting results
on how users can benefit from communication with trusted users in the federated
learning scenario. This work builds upon the fundamental algorithm framework
and theoretical guarantees for federated learning in the generic social network
scenario.
