1,328 research outputs found
Delay Minimization for Federated Learning Over Wireless Communication Networks
In this paper, the problem of delay minimization 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 parameters
to a base station (BS) which aggregates the local FL models and broadcasts the
aggregated FL model back to all the users. Since FL involves learning model
exchanges between the users and the BS, both computation and communication
latencies are determined by the required learning accuracy level, which affects
the convergence rate of the FL algorithm. This joint learning and communication
problem is formulated as a delay minimization problem, where it is proved that
the objective function is a convex function of the learning accuracy. Then, a
bisection search algorithm is proposed to obtain the optimal solution.
Simulation results show that the proposed algorithm can reduce delay by up to
27.3% compared to conventional FL methods.Comment: arXiv admin note: substantial text overlap with arXiv:1911.0241
Joint Optimization of Energy Consumption and Completion Time in Federated Learning
Federated Learning (FL) is an intriguing distributed machine learning
approach due to its privacy-preserving characteristics. To balance the
trade-off between energy and execution latency, and thus accommodate different
demands and application scenarios, we formulate an optimization problem to
minimize a weighted sum of total energy consumption and completion time through
two weight parameters. The optimization variables include bandwidth,
transmission power and CPU frequency of each device in the FL system, where all
devices are linked to a base station and train a global model collaboratively.
Through decomposing the non-convex optimization problem into two subproblems,
we devise a resource allocation algorithm to determine the bandwidth
allocation, transmission power, and CPU frequency for each participating
device. We further present the convergence analysis and computational
complexity of the proposed algorithm. Numerical results show that our proposed
algorithm not only has better performance at different weight parameters (i.e.,
different demands) but also outperforms the state of the art.Comment: This paper appears in the Proceedings of IEEE International
Conference on Distributed Computing Systems (ICDCS) 2022. Please feel free to
contact us for questions or remark
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
Time Minimization in Hierarchical Federated Learning
Federated Learning is a modern decentralized machine learning technique where
user equipments perform machine learning tasks locally and then upload the
model parameters to a central server. In this paper, we consider a 3-layer
hierarchical federated learning system which involves model parameter exchanges
between the cloud and edge servers, and the edge servers and user equipment. In
a hierarchical federated learning model, delay in communication and computation
of model parameters has a great impact on achieving a predefined global model
accuracy. Therefore, we formulate a joint learning and communication
optimization problem to minimize total model parameter communication and
computation delay, by optimizing local iteration counts and edge iteration
counts. To solve the problem, an iterative algorithm is proposed. After that, a
time-minimized UE-to-edge association algorithm is presented where the maximum
latency of the system is reduced. Simulation results show that the global model
converges faster under optimal edge server and local iteration counts. The
hierarchical federated learning latency is minimized with the proposed
UE-to-edge association strategy.Comment: This paper appears in the Proceedings of 2022 ACM/IEEE Symposium on
Edge Computing (SEC). Please feel free to contact us for questions or remark
A Joint Learning and Communications Framework for Federated Learning over Wireless Networks
In this paper, the problem of training federated learning (FL) algorithms
over a realistic wireless network is studied. In particular, in the considered
model, wireless users execute an FL algorithm while training their local FL
models using their own data and transmitting the trained local FL models to a
base station (BS) that will generate a global FL model and send it back to the
users. Since all training parameters are transmitted over wireless links, the
quality of the training will be affected by wireless factors such as packet
errors and the availability of wireless resources. Meanwhile, due to the
limited wireless bandwidth, the BS must select an appropriate subset of users
to execute the FL algorithm so as to build a global FL model accurately. This
joint learning, wireless resource allocation, and user selection problem is
formulated as an optimization problem whose goal is to minimize an FL loss
function that captures the performance of the FL algorithm. To address this
problem, a closed-form expression for the expected convergence rate of the FL
algorithm is first derived to quantify the impact of wireless factors on FL.
Then, based on the expected convergence rate of the FL algorithm, the optimal
transmit power for each user is derived, under a given user selection and
uplink resource block (RB) allocation scheme. Finally, the user selection and
uplink RB allocation is optimized so as to minimize the FL loss function.
Simulation results show that the proposed joint federated learning and
communication framework can reduce the FL loss function value by up to 10% and
16%, respectively, compared to: 1) An optimal user selection algorithm with
random resource allocation and 2) a standard FL algorithm with random user
selection and resource allocation.Comment: This paper has been accepted by IEEE Transactions on Wireless
Communication
Mobile Edge Computing
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