Artificial intelligence of things at the edge: Scalable and efficient distributed learning for massive scenarios

Federated Learning (FL) is a distributed optimization method in which multiple client nodes
collaborate to train a machine learning model without sharing data with a central server.
However, communication between numerous clients and the central aggregation server to share
model parameters can cause several problems, including latency and network congestion. To
address these issues, we propose a scalable communication infrastructure based on Information-
Centric Networking built and tested on Apache Kafka®. We design an algorithm for effective
client participation in FL within the proposed architecture. The proposed architecture consists
of a two-tier communication model. In the first layer, client updates are cached at the edge
between clients and the server, while in the second layer, the server computes global model
updates by aggregating the cached models. The data stored in the intermediate nodes at the
edge enables reliable and effective data transmission and solves the problem of intermittent
connectivity of mobile nodes. While many local model updates provided by clients can result in
a more accurate global model in FL, they can also result in massive data traffic that negatively
impacts congestion at the edge. Therefore, it is necessary to avoid congestion at the edge and the
resulting transmission delays to the cloud server. For this reason, we couple a client selection
procedure based on a congestion control mechanism at the edge for the given architecture of FL.
The proposed algorithm selects a subset of clients based on their resources through a time-based
backoff system to account for the time-averaged accuracy of FL while limiting the traffic load.
Experiments show that our proposed architecture has an improvement of over 40% over the
network-centric based FL architecture, i.e., Flower. The architecture also provides scalability
and reliability in the case of mobile nodes. It also improves client resource utilization, avoids
overflow, and ensures fairness in client selection. The experiments show that the proposed
algorithm leads to the desired client selection patterns and is adaptable to changing network
environments.