Mobile edge learning is an emerging technique that enables distributed edge
devices to collaborate in training shared machine learning models by exploiting
their local data samples and communication and computation resources. To deal
with the straggler dilemma issue faced in this technique, this paper proposes a
new device to device enabled data sharing approach, in which different edge
devices share their data samples among each other over communication links, in
order to properly adjust their computation loads for increasing the training
speed. Under this setup, we optimize the radio resource allocation for both
data sharing and distributed training, with the objective of minimizing the
total training delay under fixed numbers of local and global iterations.
Numerical results show that the proposed data sharing design significantly
reduces the training delay, and also enhances the training accuracy when the
data samples are non independent and identically distributed among edge
devices.

本文提出使用不同边缘设备间通过通信链接共享数据样本的新设备到设备的数据共享方法，在此设置下，优化了无线电资源分配以减少训练时延，并在数据样本在边缘设备之间非独立且非同分布时增加了训练精度。

边缘无线网络分布式机器学习的 D2D 数据共享

D2D-Enabled Data Sharing for Distributed Machine Learning at Wireless  Network Edge

This paper aims to establish a new optimization paradigm for implementing
realistic distributed learning algorithms, with performance guarantees, on
wireless edge nodes with heterogeneous computing and communication capacities.
We will refer to this new paradigm as `Mobile Edge Learning (MEL)'. The problem
of dynamic task allocation for MEL is considered in this paper with the aim to
maximize the learning accuracy, while guaranteeing that the total times of data
distribution/aggregation over heterogeneous channels, and local computing
iterations at the heterogeneous nodes, are bounded by a preset duration. The
problem is first formulated as a quadratically-constrained integer linear
problem. Being an NP-hard problem, the paper relaxes it into a non-convex
problem over real variables. We thus proposed two solutions based on deriving
analytical upper bounds of the optimal solution of this relaxed problem using
Lagrangian analysis and KKT conditions, and the use of suggest-and-improve
starting from equal batch allocation, respectively. The merits of these
proposed solutions are exhibited by comparing their performances to both
numerical approaches and the equal task allocation approach.

本论文旨在为在具有异构计算和通信能力的无线边缘节点上实现具有性能保证的逼真分布式学习算法建立新的优化范例，并考虑了 MEL 的动态任务分配问题，以最大化学习准确性，同时保证在预设时间内，异构通道上数据分布 / 聚合的总时间和异构节点上的局部计算迭代次数被限制。通过比较这些提议解决方案的性能与数值方法和等任务分配方法，展示了这些提议解决方案的优点。