Federated Learning in Wireless Networks via Over-the-Air Computations
Halil Yigit Oksuz Fabio Molinari Henning Sprekeler Jörg Raisch
In a multi-agent system, agents can cooperatively learn a model from data by
exchanging their estimated model parameters, without the need to exchange the
locally available data used by the agents. This strategy, often called
federated learning, is mainly employed for two reasons: (i) improving
resource-efficiency by avoiding to share potentially large datasets and (ii)
guaranteeing privacy of local agents' data. Efficiency can be further increased
by adopting a beyond-5G communication strategy that goes under the name of
Over-the-Air Computation. This strategy exploits the interference property of
the wireless channel. Standard communication schemes prevent interference by
enabling transmissions of signals from different agents at distinct time or
frequency slots, which is not required with Over-the-Air Computation, thus
saving resources. In this case, the received signal is a weighted sum of
transmitted signals, with unknown weights (fading channel coefficients). State
of the art papers in the field aim at reconstructing those unknown
coefficients. In contrast, the approach presented here does not require
reconstructing channel coefficients by complex encoding-decoding schemes. This
improves both efficiency and privacy.