Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Optimization for Client-Server Architecture with Negative
  Gradient Weights   Availability of both massive datasets and computing resources have made
machine learning and predictive analytics extremely pervasive. In this work we
present a synchronous algorithm and architecture for distributed optimization
motivated by privacy requirements posed by applications in machine learning. We
present an algorithm for the recently proposed multi-parameter-server
architecture. We consider a group of parameter servers that learn a model based
on randomized gradients received from clients. Clients are computational
entities with private datasets (inducing a private objective function), that
evaluate and upload randomized gradients to the parameter servers. The
parameter servers perform model updates based on received gradients and share
the model parameters with other servers. We prove that the proposed algorithm
can optimize the overall objective function for a very general architecture
involving $C$ clients connected to $S$ parameter servers in an arbitrary time
varying topology and the parameter servers forming a connected network.

30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int