Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Data-Free Evaluation of User Contributions in Federated Learning   Federated learning (FL) trains a machine learning model on mobile devices in
a distributed manner using each device's private data and computing resources.
A critical issues is to evaluate individual users' contributions so that (1)
users' effort in model training can be compensated with proper incentives and
(2) malicious and low-quality users can be detected and removed. The
state-of-the-art solutions require a representative test dataset for the
evaluation purpose, but such a dataset is often unavailable and hard to
synthesize. In this paper, we propose a method called Pairwise Correlated
Agreement (PCA) based on the idea of peer prediction to evaluate user
contribution in FL without a test dataset. PCA achieves this using the
statistical correlation of the model parameters uploaded by users. We then
apply PCA to designing (1) a new federated learning algorithm called Fed-PCA,
and (2) a new incentive mechanism that guarantees truthfulness. We evaluate the
performance of PCA and Fed-PCA using the MNIST dataset and a large industrial
product recommendation dataset. The results demonstrate that our Fed-PCA
outperforms the canonical FedAvg algorithm and other baseline methods in
accuracy, and at the same time, PCA effectively incentivizes users to behave
truthfully.

30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom