10 research outputs found
Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMO
Cell-free massive MIMO is emerging as a promising technology for future
wireless communication systems, which is expected to offer uniform coverage and
high spectral efficiency compared to classical cellular systems. We study in
this paper how cell-free massive MIMO can support federated edge learning.
Taking advantage of the additive nature of the wireless multiple access
channel, over-the-air computation is exploited, where the clients send their
local updates simultaneously over the same communication resource. This
approach, known as over-the-air federated learning (OTA-FL), is proven to
alleviate the communication overhead of federated learning over wireless
networks. Considering channel correlation and only imperfect channel state
information available at the central server, we propose a practical
implementation of OTA-FL over cell-free massive MIMO. The convergence of the
proposed implementation is studied analytically and experimentally, confirming
the benefits of cell-free massive MIMO for OTA-FL
Energy Efficient Federated Learning Over Wireless Communication Networks
In this paper, the problem of energy efficient transmission and computation
resource allocation for federated learning (FL) over wireless communication
networks is investigated. In the considered model, each user exploits limited
local computational resources to train a local FL model with its collected data
and, then, sends the trained FL model to a base station (BS) which aggregates
the local FL model and broadcasts it back to all of the users. Since FL
involves an exchange of a learning model between users and the BS, both
computation and communication latencies are determined by the learning accuracy
level. Meanwhile, due to the limited energy budget of the wireless users, both
local computation energy and transmission energy must be considered during the
FL process. This joint learning and communication problem is formulated as an
optimization problem whose goal is to minimize the total energy consumption of
the system under a latency constraint. To solve this problem, an iterative
algorithm is proposed where, at every step, closed-form solutions for time
allocation, bandwidth allocation, power control, computation frequency, and
learning accuracy are derived. Since the iterative algorithm requires an
initial feasible solution, we construct the completion time minimization
problem and a bisection-based algorithm is proposed to obtain the optimal
solution, which is a feasible solution to the original energy minimization
problem. Numerical results show that the proposed algorithms can reduce up to
59.5% energy consumption compared to the conventional FL method.Comment: In IEEE TW
Federated Edge Learning with Misaligned Over-The-Air Computation
Over-the-air computation (OAC) is a promising technique to realize fast model
aggregation in the uplink of federated edge learning. OAC, however, hinges on
accurate channel-gain precoding and strict synchronization among the edge
devices, which are challenging in practice. As such, how to design the maximum
likelihood (ML) estimator in the presence of residual channel-gain mismatch and
asynchronies is an open problem. To fill this gap, this paper formulates the
problem of misaligned OAC for federated edge learning and puts forth a whitened
matched filtering and sampling scheme to obtain oversampled, but independent,
samples from the misaligned and overlapped signals. Given the whitened samples,
a sum-product ML estimator and an aligned-sample estimator are devised to
estimate the arithmetic sum of the transmitted symbols. In particular, the
computational complexity of our sum-product ML estimator is linear in the
packet length and hence is significantly lower than the conventional ML
estimator. Extensive simulations on the test accuracy versus the average
received energy per symbol to noise power spectral density ratio (EsN0) yield
two main results: 1) In the low EsN0 regime, the aligned-sample estimator can
achieve superior test accuracy provided that the phase misalignment is
non-severe. In contrast, the ML estimator does not work well due to the error
propagation and noise enhancement in the estimation process. 2) In the high
EsN0 regime, the ML estimator attains the optimal learning performance
regardless of the severity of phase misalignment. On the other hand, the
aligned-sample estimator suffers from a test-accuracy loss caused by phase
misalignment.Comment: 17 pages, 11 figure
Energy-Aware Analog Aggregation for Federated Learning with Redundant Data
Federated learning (FL) enables workers to learn a model collaboratively by using their local data, with the help of a parameter server (PS) for global model aggregation. The high communication cost for periodic model updates and the nonindependent and identically distributed (i.i.d.) data become major bottlenecks for FL. In this work, we consider analog aggregation to scale down the communication cost with respect to the number of workers, and introduce data redundancy to the system to deal with non-i.i.d. data. We propose an online energy-aware dynamic worker scheduling policy, which maximizes the average number of workers scheduled for gradient update at each iteration under a long-term energy constraint, and analyze its performance based on Lyapunov optimization. Experiments using MNIST dataset show that, for non-i.i.d. data, doubling data storage can improve the accuracy by 9.8 under a stringent energy budget, while the proposed policy can achieve close-to-optimal accuracy without violating the energy constraint