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Distributed Learning, Communication Complexity and Privacy
[article]
2012
arXiv
pre-print
We consider the problem of PAC-learning from distributed data and analyze fundamental communication complexity questions involved. We provide general upper and lower bounds on the amount of communication needed to learn well, showing that in addition to VC-dimension and covering number, quantities such as the teaching-dimension and mistake-bound of a class play an important role. We also present tight results for a number of common concept classes including conjunctions, parity functions, and
arXiv:1204.3514v3
fatcat:icnluq6eojhytoby63xhdbgtxa