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Communication Efficient Decentralised Machine Learning
Anastasia Koloskova, MLO, EPFL
Thursday, June 6, 2019 · 12:11 p.m. · 02m 18s
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00:00:07
well i just reject the focus something shouldn't models in the distant alexei
00:00:13
the main a feature of this until i still think is what the during
00:00:17
training but data off every utility's local and then never at least the users device
00:00:23
we're all that is not until forty and every device communicates on t. v. a small
00:00:27
number of neighbours for example on this picture on the connected devices allow the to communicate
00:00:33
this i think is very useful for example in me just in a uh this can help
00:00:38
of the two hospitals to pay in uh
00:00:41
something together without taking a sensitive data from hospitals
00:00:48
a state of about this and the last thing you got gay consist of two steps optimisation and the communication
00:00:55
during customisations that uh uh every device computes for optimal updates to all the trainable parameters
00:01:02
and the uh during communication step we want to share this updates with other devices
00:01:08
of uh because uh off and uh communication between machines is often slow
00:01:13
and the amount of trainable parameters in motion don't models is huge
00:01:18
for example a neural networks have several millions of rights it is very slow to
00:01:23
communicate to love and between devices and that is because measure bottleneck in doesn't always turning
00:01:31
in our work but this this problem and there were proposed in all this
00:01:34
in the last issue yeah okay i just recall the chalk wage should yeah
00:01:39
this can drastically the did use communication time uh using communication compression
00:01:45
the main idea for well get it is what that every
00:01:47
step but don't communicate also parameters but the only part of it
00:01:53
and we keep information about the others locally at the correct whatever set the next generations
00:01:58
and the this is my first they just in the last issue the algorithm which can conversion and the arbitrary high compression
00:02:05
and more of a cut a conversion speed is uh the same a simplistic
Conference Program
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