  Major bottlenecks of large-scale Federated Learning(FL) networks are the high
costs for communication and computation. This is due to the fact that most of
current FL frameworks only consider a star network topology where all local
trained models are aggregated at a single server (e.g., a cloud server). This
causes significant overhead at the server when the number of users are huge and
local models' sizes are large. This paper proposes a novel edge network
architecture which decentralizes the model aggregation process at the server,
thereby significantly reducing the aggregation latency of the whole network. In
this architecture, we propose a highly-effective in-network computation
protocol consisting of two components. First, an in-network aggregation process
is designed so that the majority of aggregation computations can be offloaded
from cloud server to edge nodes. Second, a joint routing and resource
allocation optimization problem is formulated to minimize the aggregation
latency for the whole system at every learning round. The problem turns out to
be NP-hard, and thus we propose a polynomial time routing algorithm which can
achieve near optimal performance with a theoretical bound. Numerical results
show that our proposed framework can dramatically reduce the network latency,
up to 4.6 times. Furthermore, this framework can significantly decrease cloud's
traffic and computing overhead by a factor of K/M, where K is the number of
users and M is the number of edge nodes, in comparison with conventional
baselines.
