Micro-UAV swarms usually generate mas-sive data when performing tasks.These data can be harnessed with various machine learning (ML) algo-rithms to improve the swarm\'s intelligence.To achieve this goal while protecting swarm data privacy,feder-ated learning (FL) has been proposed as a promising enabling technology.During the model training pro-cess of FL,the UAV may face an energy scarcity issue due to the limited battery capacity.Fortunately,this issue is potential to be tackled via simultaneous wire-less information and power transfer (SWIPT).How-ever,the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed,which motivates our work.Specifically,in this paper,we consider a micro-UAV swarm net-work consisting of one base station (BS) and multiple UAVs,where the BS uses FL to train an ML model over the data collected by the swarm.During train-ing,the BS broadcasts the model and energy simulta-neously to the UAVs via SWIPT,and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation.To improve the learning performance,we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV schedul-ing and wireless resource allocation.The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general.By exploiting its special structure property,we develop two algorithms to achieve the optimal and suboptimal solutions,re-spectively.Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups,and significantly outperforms the existing representative baselines,considered.