Table 1 presents the previous and current results strangely:
1) First of all, to compare the obtained complexity for the proposed method with the previous result in a strongly-convex concave case, it should be used standard regularization trick.
2) From my point of you, when complexity contains several terms, each of them should be added.
About Table 2, The authors claim that "The sequential version of FeDualEx leads to the stochastic dual extrapolation for CO and yields, to our knowledge, the first convergence rate for the stochastic optimization of composite SPP in non-Euclidean settings ." It is not true, there is a wide field related to operator splitting in deterministic and stochastic cases. Look at this paper please https://epubs.siam.org/doi/epdf/10.1137/20M1381678.
Also, compared to the previous works, the authors use bounded stochastic gradient assumption and homogeneity of data. In many federated learning papers, those assumptions are avoided. Despite that the authors write "Assumption e is a standard assumption", it would be better to provide analysis without it to have more generality.
In Theorem 1, and Theorem 2, the final result contains mistakes in complexity, because some of them were done in the proof.
The first mistake is made in theorem 3 and repeats in the main theorem. Please look at the last inequality on page 40:
To make $3\eta^2\beta^2 -1 \leq 0$, the stepsize should be chosen in the following way: $\eta \leq \frac{1}{\sqrt{3}\beta}$. This will change the complexity of the methods. The same was done in the proof of Theorems 1, and 2. Please see Lemma 3, 17.
The second mistake is made in the proof of Lemma 13, in the last two inequalities, where should be $\dots\sqrt{2V^l_z(\cdot)} \leq \dots \sqrt{B}$. This thing also will change the final complexity.
The appendix is hard to read in terms of the order of Lemmas. I think it would be better if the numeration of Lemmas had a strict order (for example, after Lemma 5 lemma 6 follows.)
Other things dealing with weaknesses, please, see in questions.
1) First of all, to compare the obtained complexity for the proposed method with the previous result in a strongly-convex concave case, it should be used standard regularization trick.