Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Active Federated Learning   Federated Learning allows for population level models to be trained without
centralizing client data by transmitting the global model to clients,
calculating gradients locally, then averaging the gradients. Downloading models
and uploading gradients uses the client's bandwidth, so minimizing these
transmission costs is important. The data on each client is highly variable, so
the benefit of training on different clients may differ dramatically. To
exploit this we propose Active Federated Learning, where in each round clients
are selected not uniformly at random, but with a probability conditioned on the
current model and the data on the client to maximize efficiency. We propose a
cheap, simple and intuitive sampling scheme which reduces the number of
required training iterations by 20-70% while maintaining the same model
accuracy, and which mimics well known resampling techniques under certain
conditions.

28
The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
3
The categories of this paper are: physics
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex