  With the wealth of information produced by social networks, smartphones,
medical or financial applications, speculations have been raised about the
sensitivity of such data in terms of users' personal privacy and data security.
To address the above issues, Federated Learning (FL) has been recently proposed
as a means to leave data and computational resources distributed over a large
number of nodes (clients) where a central coordinating server aggregates only
locally computed updates without knowing the original data. In this work, we
extend the FL framework by pushing forward the state the art in the field on
several dimensions: (i) unlike the original FedAvg approach relying solely on
single criteria (i.e., local dataset size), a suite of domain- and
client-specific criteria constitute the basis to compute each local client's
contribution, (ii) the multi-criteria contribution of each device is computed
in a prioritized fashion by leveraging a priority-aware aggregation operator
used in the field of information retrieval, and (iii) a mechanism is proposed
for online-adjustment of the aggregation operator parameters via a local search
strategy with backtracking. Extensive experiments on a publicly available
dataset indicate the merits of the proposed approach compared to standard
FedAvg baseline.
