  The bottleneck of distributed edge learning (DEL) over wireless has shifted
from computing to communication, primarily the aggregation-averaging (Agg-Avg)
process of DEL. The existing transmission control protocol (TCP)-based data
networking schemes for DEL are application-agnostic and fail to deliver
adjustments according to application layer requirements. As a result, they
introduce massive excess time and undesired issues such as unfairness and
stragglers. Other prior mitigation solutions have significant limitations as
they balance data flow rates from workers across paths but often incur
imbalanced backlogs when the paths exhibit variance, causing stragglers. To
facilitate a more productive DEL, we develop a hybrid multipath TCP (MPTCP) by
combining model-based and deep reinforcement learning (DRL) based MPTCP for DEL
that strives to realize quicker iteration of DEL and better fairness (by
ameliorating stragglers). Hybrid MPTCP essentially integrates two radical TCP
developments: i) successful existing model-based MPTCP control strategies and
ii) advanced emerging DRL-based techniques, and introduces a novel hybrid MPTCP
data transport for easing the communication of the Agg-Avg process. Extensive
emulation results demonstrate that the proposed hybrid MPTCP can overcome
excess time consumption and ameliorate the application layer unfairness of DEL
effectively without injecting additional inconstancy and stragglers.
