  The lottery ticket hypothesis (LTH) claims that a deep neural network (i.e.,
ground network) contains a number of subnetworks (i.e., winning tickets), each
of which exhibiting identically accurate inference capability as that of the
ground network. Federated learning (FL) has recently been applied in LotteryFL
to discover such winning tickets in a distributed way, showing higher accuracy
multi-task learning than Vanilla FL. Nonetheless, LotteryFL relies on unicast
transmission on the downlink, and ignores mitigating stragglers, questioning
scalability. Motivated by this, in this article we propose a personalized and
communication-efficient federated lottery ticket learning algorithm, coined
CELL, which exploits downlink broadcast for communication efficiency.
Furthermore, it utilizes a novel user grouping method, thereby alternating
between FL and lottery learning to mitigate stragglers. Numerical simulations
validate that CELL achieves up to 3.6% higher personalized task classification
accuracy with 4.3x smaller total communication cost until convergence under the
CIFAR-10 dataset.
