Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed
Optimal Aggregation
algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a
P
robabilistic
N
ode
S
election framework (
FedPNS
) to dynamically change the probability for each node to be selected based on the output of
Optimal Aggregation
.
FedPNS
can preferentially select nodes that propel faster model convergence. The convergence rate improvement of
FedPNS
over the commonly adopted Federated Averaging (
FedAvg
) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of
FedPNS
in accelerating the FL convergence rate, as compared to
FedAvg
with random node selection.