1 research outputs found
Beamforming and Device Selection Design in Federated Learning with Over-the-air Aggregation
Federated learning (FL) with over-the-air computation can efficiently utilize
the communication bandwidth but is susceptible to analog aggregation error.
Excluding those devices with weak channel conditions can reduce the aggregation
error, but it also limits the amount of local training data for FL, which can
reduce the training convergence rate. In this work, we jointly design uplink
receiver beamforming and device selection for over-the-air FL over time-varying
wireless channels to maximize the training convergence rate. We reformulate
this stochastic optimization problem into a mixed-integer program using an
upper bound on the global training loss over communication rounds. We then
propose a Greedy Spatial Device Selection (GSDS) approach, which uses a
sequential procedure to select devices based on a measure capturing both the
channel strength and the channel correlation to the selected devices. We show
that given the selected devices, the receiver beamforming optimization problem
is equivalent to downlink single-group multicast beamforming. To reduce the
computational complexity, we also propose an Alternating-optimization-based
Device Selection and Beamforming (ADSBF) approach, which solves the receiver
beamforming and device selection subproblems alternatingly. In particular,
despite the device selection being an integer problem, we are able to develop
an efficient algorithm to find its optimal solution.
Simulation results with real-world image classification demonstrate that our
proposed methods achieve faster convergence with significantly lower
computational complexity than existing alternatives. Furthermore, although
ADSBF shows marginally inferior performance to GSDS, it offers the advantage of
lower computational complexity when the number of devices is large.Comment: 12 pages, 8 figure