Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Convergence and Accuracy Trade-Offs in Federated Learning and
  Meta-Learning   We study a family of algorithms, which we refer to as local update methods,
generalizing many federated and meta-learning algorithms. We prove that for
quadratic models, local update methods are equivalent to first-order
optimization on a surrogate loss we exactly characterize. Moreover, fundamental
algorithmic choices (such as learning rates) explicitly govern a trade-off
between the condition number of the surrogate loss and its alignment with the
true loss. We derive novel convergence rates showcasing these trade-offs and
highlight their importance in communication-limited settings. Using these
insights, we are able to compare local update methods based on their
convergence/accuracy trade-off, not just their convergence to critical points
of the empirical loss. Our results shed new light on a broad range of
phenomena, including the efficacy of server momentum in federated learning and
the impact of proximal client updates.

26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
25
The categories of this paper are: patt-sol
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph