Expediting In-Network Federated Learning by Voting-Based Consensus Model
  Compression
Xiaoxin Su Yipeng Zhou Laizhong Cui Song Guo
Recently, federated learning (FL) has gained momentum because of its
capability in preserving data privacy. To conduct model training by FL,
multiple clients exchange model updates with a parameter server via Internet.
To accelerate the communication speed, it has been explored to deploy a
programmable switch (PS) in lieu of the parameter server to coordinate clients.
The challenge to deploy the PS in FL lies in its scarce memory space,
prohibiting running memory consuming aggregation algorithms on the PS. To
overcome this challenge, we propose Federated Learning in-network Aggregation
with Compression (FediAC) algorithm, consisting of two phases: client voting
and model aggregating. In the former phase, clients report their significant
model update indices to the PS to estimate global significant model updates. In
the latter phase, clients upload global significant model updates to the PS for
aggregation. FediAC consumes much less memory space and communication traffic
than existing works because the first phase can guarantee consensus compression
across clients. The PS easily aligns model update indices to swiftly complete
aggregation in the second phase. Finally, we conduct extensive experiments by
using public datasets to demonstrate that FediAC remarkably surpasses the
state-of-the-art baselines in terms of model accuracy and communication
traffic.