Recent advances in Federated Learning (FL) have paved the way towards the
design of novel strategies for solving multiple learning tasks simultaneously,
by leveraging cooperation among networked devices. Multi-Task Learning (MTL)
exploits relevant commonalities across tasks to improve efficiency compared
with traditional transfer learning approaches. By learning multiple tasks
jointly, significant reduction in terms of energy footprints can be obtained.
This article provides a first look into the energy costs of MTL processes
driven by the Model-Agnostic Meta-Learning (MAML) paradigm and implemented in
distributed wireless networks. The paper targets a clustered multi-task network
setup where autonomous agents learn different but related tasks. The MTL
process is carried out in two stages: the optimization of a meta-model that can
be quickly adapted to learn new tasks, and a task-specific model adaptation
stage where the learned meta-model is transferred to agents and tailored for a
specific task. This work analyzes the main factors that influence the MTL
energy balance by considering a multi-task Reinforcement Learning (RL) setup in
a robotized environment. Results show that the MAML method can reduce the
energy bill by at least 2 times compared with traditional approaches without
inductive transfer. Moreover, it is shown that the optimal energy balance in
wireless networks depends on uplink/downlink and sidelink communication
efficiencies.

本文研究了在分布式无线网络中使用 Model-Agnostic Meta-Learning（MAML）范式驱动的多任务学习（MTL）过程的能量成本，发现该方法可以通过联邦学习减少能量开支至少两倍，并且无线网络的最佳能量平衡取决于上行 / 下行和侧向链接通信效率。