  Federated learning (FL) was designed to enable mobile phones to
collaboratively learn a global model without uploading their private data to a
cloud server. However, exiting FL protocols has a critical communication
bottleneck in a federated network coupled with privacy concerns, usually
powered by a wide-area network (WAN). Such a WAN-driven FL design leads to
significantly high cost and much slower model convergence. In this work, we
propose an efficient FL protocol, which involves a hierarchical aggregation
mechanism in the local-area network (LAN) due to its abundant bandwidth and
almost negligible monetary cost than WAN. Our proposed FL can accelerate the
learning process and reduce the monetary cost with frequent local aggregation
in the same LAN and infrequent global aggregation on a cloud across WAN. We
further design a concrete FL platform, namely LanFL, that incorporates several
key techniques to handle those challenges introduced by LAN: cloud-device
aggregation architecture, intra-LAN peer-to-peer (p2p) topology generation,
inter-LAN bandwidth capacity heterogeneity. We evaluate LanFL on 2 typical
Non-IID datasets, which reveals that LanFL can significantly accelerate FL
training (1.5x-6.0x), save WAN traffic (18.3x-75.6x), and reduce monetary cost
(3.8x-27.2x) while preserving the model accuracy.

Machine Learning;Distributed, Parallel, and Cluster Computing