Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluation and Optimization of Distributed Machine Learning Techniques
  for Internet of Things   Federated learning (FL) and split learning (SL) are state-of-the-art
distributed machine learning techniques to enable machine learning training
without accessing raw data on clients or end devices. However, their
\emph{comparative training performance} under real-world resource-restricted
Internet of Things (IoT) device settings, e.g., Raspberry Pi, remains barely
studied, which, to our knowledge, have not yet been evaluated and compared,
rendering inconvenient reference for practitioners. This work firstly provides
empirical comparisons of FL and SL in real-world IoT settings regarding (i)
learning performance with heterogeneous data distributions and (ii) on-device
execution overhead. Our analyses in this work demonstrate that the learning
performance of SL is better than FL under an imbalanced data distribution but
worse than FL under an extreme non-IID data distribution. Recently, FL and SL
are combined to form splitfed learning (SFL) to leverage each of their benefits
(e.g., parallel training of FL and lightweight on-device computation
requirement of SL). This work then considers FL, SL, and SFL, and mount them on
Raspberry Pi devices to evaluate their performance, including training time,
communication overhead, power consumption, and memory usage. Besides
evaluations, we apply two optimizations. Firstly, we generalize SFL by
carefully examining the possibility of a hybrid type of model training at the
server-side. The generalized SFL merges sequential (dependent) and parallel
(independent) processes of model training and is thus beneficial for a system
with large-scaled IoT devices, specifically at the server-side operations.
Secondly, we propose pragmatic techniques to substantially reduce the
communication overhead by up to four times for the SL and (generalized) SFL.

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The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: q-bio
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The categories of this paper are: hep-lat
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The categories of this paper are: alg-geom
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: stat
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The categories of this paper are: chao-dyn
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The categories of this paper are: cmp-lg
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The categories of this paper are: q-alg
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The categories of this paper are: math
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The categories of this paper are: hep-th