Paper: Distribution-Free Fair Federated Learning with Small Samples (ICLR-2024)

Reviewer comment (Novelty):
My primary concern regarding this paper centers on its contribution when compared with previous work, particularly the FaiREE algorithm designed for centralized learning. Based on my interpretation, the overall process appears quite similar, with the primary distinction being the distributed aggregation process.

Author response:
For Weakness and Question, regarding the differences between FedFaiREE and FaiREE, in light of your comment, we have incorporated the following discussion highlighting the differences between FedFaiREE and FaiREE:

“Regarding the differences between FedFaiREE and FaiREE, several pivotal distinctions become evident. Primarily, FedFaiREE demonstrates superior adaptability for practical applications. Notably, it incorporates mechanisms to handle label shift scenarios, ensuring model robustness within such distributions, as elucidated in Section 5.1. Furthermore, it's worth noting that FedFaiREE extends considerations to encompass multiple sensitive groups and multiple labels, aligning more closely with practical real-world application scenarios, as discussed in Appendix D.

Another critical difference lies in the setting: FaiREE operates in a centralized environment, assuming homogeneous data across all clients. In contrast, FedFaiREE is expressly tailored for decentralized settings, acknowledging client heterogeneity and effectively addressing the challenges stemming from diverse data distributions and sizes across clients. This tailored approach significantly enhances its adaptability and robustness across various scenarios.

Lastly, while FaiREE relies on specific centralized quantile estimation methods, FedFaiREE adopts approximate quantiles. This adaptation not only facilitates adaptation to distributed data but also fortifies the method's robustness and adaptability.”

Reviewer comment (Theory):
Help me understand the theoretical results: How tight is the bound that is derived in (7)? If the optimal classifier is indeed unfair, how can you still achieve an optimal misclassification error with the DEOO constraint?

Author response:
In our paper, the fairness-constrained Bayes-optimal classifier is defined as the classifier with the highest accuracy under the desired DEOO constraints.  Notably, the construction of the fairness-constrained Bayes-optimal classifier involves employing group-wise shifts derived from the unconstrained Bayes-optimal classifier[1]. This approach serves as a motivation behind the development of the FedFaiREE pipeline. The major results in equation (7) encompass two aspects: one extends from empirical misclassification to global misclassification, and the other progresses from the optimal global misclassification within the candidate set to the Bayes optimal classifier. The former involves error bounds concerning approximate quantiles, the use of Hoeffding's inequality for empirical estimation of $p_a$ and $p_{Y,a}$ and embedding these empirical estimations into the error. In this part, only the embedding could potentially be further optimized, with an upper bound space of at most O($\epsilon$). The bounds in the latter aspect mainly involve estimates of distribution function characteristics (Lemma A.4).

Reviewer comment (Experiments):
Why is the same $\alpha$ used for all experiments on a dataset (i.e., 0.1 for Adult and 0.15 for Compas)? How should this alpha be set in practice?

Author response:
For Questions 3 and 4, we appreciate your comment, and in response, we have included additional experiments featuring different fairness constraint parameters $\alpha$ and various confidence levels $\beta$ in Appendix C.3. In practice, setting $\alpha$ should align with the fairness requirement, but enforcing a strict fairness constraint might lead to a substantial loss in accuracy (consider choosing both $k_0$ and $k_1$ as 1). For instance, in the experiment on Compas, setting $\alpha$ to 0.13 yields an accuracy of approximately 66%, and it increases marginally as $\alpha$ grows. However, with $\alpha$ set to 0.12, the accuracy drops to below 60%. 
 
We value your feedback and have made the necessary additions and adjustments accordingly.

Reviewer comment (Experiments):
What is the trend of accuracy (ACC) when we shrink $\alpha$? What is a good point to stop (for $\alpha$) in order to balance accuracy and fairness? What is the lowest value of $\alpha$ we can go?

Author response:
For Questions 3 and 4, we appreciate your comment, and in response, we have included additional experiments featuring different fairness constraint parameters $\alpha$ and various confidence levels $\beta$ in Appendix C.3. In practice, setting $\alpha$ should align with the fairness requirement, but enforcing a strict fairness constraint might lead to a substantial loss in accuracy (consider choosing both $k_0$ and $k_1$ as 1). For instance, in the experiment on Compas, setting $\alpha$ to 0.13 yields an accuracy of approximately 66%, and it increases marginally as $\alpha$ grows. However, with $\alpha$ set to 0.12, the accuracy drops to below 60%. 
 
We value your feedback and have made the necessary additions and adjustments accordingly.

Reviewer comment (Reproducibility):
Is the minimization of Eq. 5 done in a greedy manner? Do you evaluate all of the possibilities in K?

Author response:
One of the simplest methods involves constructing K is by exhaustively searching for $k_0$ and $k_1$, which has computational complexity of $O(n^2)$. To further reduce the computational cost, in the Appendix C.1, we introduce a simplified approach. We efficiently search for $k_0$ and $k_1$ by utilizing the relationship between the optimal classifier's thresholds, $t_0$ and $t_1$. This significantly simplifies the construction of K in practice (complexity O($n^{*,1}$)).

Reviewer comment (Writing):
What is the global rank of a rank (i.e., last line in Step 2 Section 3.1)?

Author response:
In Proposition 3.1 of Section 3.2 in the original paper, we defined the local rank **k**. For any global rank k, we consider the vector composed of its corresponding values at the respective ranks (rounded down) in each local dataset, denoted as **k**. In practice, due to the subsequent introduction of a distributed algorithm, **k** represents estimates of these local ranks.

Reviewer comment (Novelty):
How does your method connect with Hardt et al. 2016, which is a centralized post-processing fairness technique that also relies on finding a threshold per sensitive group to satisfy a fairness criteria?

Author response:
Comparing to the method proposed in Hardt et al. 2016, we are similar in the sense that both methods utilize scores to search for group-wise different thresholds. However, the search method is different. First of all, our method differs in that it provides provable control of fairness metrics (like DEOO with alpha-tolorance). This allows for a better control in trade-off between fairness and accuracy. Secondly, besides the difference in the design for decentralized processing and handling client heterogeneity, the method proposed in Hardt et al. 2016 does not have rigorous fair guarantees under the small-sample and distribution-free setting.

Reviewer comment (Novelty):
I find the novelty and contributions of this work to be limited, given that the main objective, formal guarantees, and algorithm are very similar to [1]. Moreover, the algorithm operates under the assumption of full client participation, a condition that may not always align with reality in FL settings.

Author response:
For Weakness 1 and Question 1, in light of your comment, we added the following discussion to Appendix E and highlight the differences:

>Regarding the differences between FedFaiREE and FaiREE[1], several pivotal distinctions become evident. Primarily, FedFaiREE demonstrates superior adaptability for practical applications. Notably, it incorporates mechanisms to handle label shift scenarios, ensuring model robustness within such distributions, as elucidated in Section 5.1. Furthermore, it's worth noting that FedFaiREE extends considerations to encompass multiple sensitive groups and multiple labels, aligning more closely with practical real-world application scenarios (see more details in Appendix D).
>
>Another critical difference lies in the setting: FaiREE operates in a centralized environment, assuming homogeneous data across all clients. In contrast, FedFaiREE is expressly tailored for decentralized settings, acknowledging client heterogeneity and effectively addressing the challenges stemming from diverse data distributions and sizes across clients. This tailored approach significantly enhances its adaptability and robustness across various scenarios.
>
>Lastly, while FaiREE relies on specific centralized quantile estimation methods, FedFaiREE adopts approximate quantiles. This adaptation not only facilitates adaptation to distributed data but also fortifies the method's robustness and adaptability.’’

At the same time, regarding the label shift scenario in Section 5.1, it can be understood as a guarantee for a client that has not participated in our training.

Reviewer comment (Theory):
The proposed problem and guarantees rely on the assumption of binary target and attribute/group variables, which is a restrictive assumption. This raises questions about the actual utility of this approach and what are its guarantees in more realistic scenarios (e.g., multiple sensitive groups and multiclass problems). I note that extending the work to multiclass problems is also identified by the authors, but there are existing works that address multiple attributes for these fairness metrics, e.g., [3].

Author response:
We appreciate your suggestion, and we have included a discussion on multiple sensitive groups and multiple labels in Appendix D. 

In the case of multiple sensitive groups, referring to the DEOO concept (defined in equation (1) in the paper), we primarily consider the maximum value of DEOO between these protected groups and the unprotected residual portions to control the overall relaxation of Equality of Opportunity (see Definition D.1 for the multi-group version). At this point, similar to previous notions, we introduce fairness control by simultaneously managing multiple DEOOs and ensuring accuracy by incorporating considerations of $p_a$ and $p_{Y,a}$ across multiple groups. 

For the multi-label situation, we are inspired by [6] and consider the scenario where labels are m-dimensional vectors, where each component is binary. The results in Sections 3 and 4 of the article can be viewed as the case when m=1. In extending the one-dimensional results to higher dimensions, the main differences are as follows: 

1. The different dimensions of labels may not be independent, hence, we introduce conditional distribution to address this. We estimate the second dimension based on the first dimension, and the third dimension based on both the first and second dimensions. This process is repeated for the remaining dimensions.

2. Q-digest cannot handle estimation of multi-dimensional distributions; therefore, we consider an iterative version of q-digest. First, we construct a q-digest based on the first dimension, then on the leaf nodes, we build q-digest based on the second dimension (the estimation error of quantiles in different dimensions differs).


3. In estimating accuracy, when extending from one dimension to higher dimensions, a closed interval in one dimension may no longer be a closed interval. Therefore, we have to consider its projection onto a closed interval in one dimension among the m dimensions.

Reviewer comment (Novelty):
The paper misses discussion and comparison to other works proposing the same idea -- i.e., how to optimize a fairness metric and produce results akin to centralized ML using the local information from clients (e.g., [2] and [4]). Also, while the authors briefly mention [5], [4] a more explicit discussion of these proposed method's conceptual differences would benefit the paper.

Author response:
Concerning Weakness 3 and Question 2, we are grateful for your insights, and we have made modifications in the related work section, added discussion of comparison in Appendix E.1 and added discussion of the fairness metric in Appendix B.3.

More specifically, in Appendix B.3, we add:

>**Definition** B.7
A classifier h satisfies Bounded Group Loss (BGL) at level $\zeta$ under distribution $\mathcal{D}$ if for all $a \in A$, we have $\mathbb{E}[l(h(x), y) \mid A=a] \leq \zeta$.
>
>**Definition** B.8
    A classifier $h$ satisfies Conditional Bounded Group Loss (CBGL) for $y \in Y$ at level $\zeta_y$ under distribution $\mathcal{D}$ if for all $a \in A$, we have $\mathbb{E}[l(h(x), y) \mid A=a, Y=y] \leq \zeta_y$.
>
>When considering y as a binary variable and the loss function l being the 0-1 loss function, BGL is equivalent to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=a] \leq \zeta,$$
>holding for any a, whereas Demographic Parity refers to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=0]=\mathbb{P}[\hat{y}\neq y |\mid A=1].$$
>In this context, BGL can be understood as a relaxation of Demographic Parity.
>
>Similarly, when considering y as a binary variable and the loss function l being the 0-1 loss function, CBGL is equivalent to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=a, Y=y] \leq \zeta_y,$$
>holding for any a, whereas Equalized Odds refers to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=0,Y=y]=\mathbb{P}[\hat{y}\neq y |\mid A=1,Y=y].$$
>In this context, CBGL can be understood as a relaxation of Equalized Odds.

Similarly, by **Definition** B.9，the metric that [2] considers can also be understood as a relaxation of Demographic Parity in the context of considering y as a binary variable and the loss function l being the 0-1 loss function. Due to the complexity of the formula, we do not present it here.

In Appendix E.1, we add:
>Differences between FedFaiREE and other fair federated learning methods lie in their approach to addressing fairness concerns. Many methods, akin to this paper, extend the principles of centralized machine learning to decentralized settings, such as FedFB[3], FedMinMax[2], PFFL[4], and others. These methods primarily focus on introducing fairness penalties in the objective functions and incorporate client reweighting schemes and terms (in objective functions) reweighting schemes that consider global or local fairness. The key divergence between our approach and these methods is that the latter typically converge and provide fairness guarantees only in large-sample scenarios, lacking assurances for fairness in small-sample situations, especially under distribution-free assumptions. Empirical results from Table 1 in this paper demonstrate that compared to FedFaiREE, methods like FedFB, FairFed[5] are not as effective in controlling fairness in small-sample scenarios. Furthermore, as these methods are predominantly in-processing techniques, while FedFaiREE falls under post-processing methods, there is a potential for further integration to achieve improved fairness guarantees as shown in our experiments. Moreover, another significant characteristic of FedFaiREE is its capability to adjust the trade-off between fairness and accuracy according to specific fairness constraints. This control capacity has been demonstrated in numerous experiments, showcasing an ability that other methods lack.

Reviewer comment (Experiments):
The experimental section requires enhancements: (1) the paper performs experiments using only two datasets, (2) important experimental details are missing, (e.g., the number of clients used in the experiments, standard deviation for each result, Dirichlet distribution parameter values that were explored, what is the sensitive group for each dataset etc.), (3) the comparison to AFL which optimizes for client-fairness (i.e. a different fairness concept in FL) should be justified.

Author response:
Addressing Weakness 4, Question 4, and Question 5:
    1. Due to time constraints, we have only incorporated another dataset, ACSIncome. We will include more datasets, including CelebA, Bank, and Dutch as suggested in our final version.
    2. Thank you for your suggestion. We have updated Table 2 to include the number of clients,Table 4, 5 to reflect the standard deviations of metrics and added Table 6 to reflect standard deviations of metrics presented in Tables 1 (due to the space limitation). The sensitive group for each dataset is outlined in the dataset information in the EXPERIMENT Section. To enhance clarity, we have also included it in Table 2. The choice of the Dirichlet distribution parameter is based on available set segmentation requirements (ensuring a nonempty test set). We also conducted an experiment using a different Dirichlet distribution parameter on Adult as it shows in Appendix C.3.
    3. Although AFL is a client-fairness method, our intention in using it is for an initialization of our proposed FedFaiREE method. It serves as an input to demonstrate the broad applicability of FedFaiREE that it can take any method as input, and ensure the resulting classifier, after post-processing, satisfies fairness under the federated learning setting even with finite-sample.

Reviewer comment (Novelty):
Can you please give more insights (than the ones at the beginning of section 3.2) on why and how the proposed method differs from [1]?

Author response:
For Weakness 1 and Question 1, in light of your comment, we added the following discussion to Appendix E and highlight the differences:

>Regarding the differences between FedFaiREE and FaiREE[1], several pivotal distinctions become evident. Primarily, FedFaiREE demonstrates superior adaptability for practical applications. Notably, it incorporates mechanisms to handle label shift scenarios, ensuring model robustness within such distributions, as elucidated in Section 5.1. Furthermore, it's worth noting that FedFaiREE extends considerations to encompass multiple sensitive groups and multiple labels, aligning more closely with practical real-world application scenarios (see more details in Appendix D).
>
>Another critical difference lies in the setting: FaiREE operates in a centralized environment, assuming homogeneous data across all clients. In contrast, FedFaiREE is expressly tailored for decentralized settings, acknowledging client heterogeneity and effectively addressing the challenges stemming from diverse data distributions and sizes across clients. This tailored approach significantly enhances its adaptability and robustness across various scenarios.
>
>Lastly, while FaiREE relies on specific centralized quantile estimation methods, FedFaiREE adopts approximate quantiles. This adaptation not only facilitates adaptation to distributed data but also fortifies the method's robustness and adaptability.’’

At the same time, regarding the label shift scenario in Section 5.1, it can be understood as a guarantee for a client that has not participated in our training.

Reviewer comment (Novelty):
How does this work compare with the related works [2],[3],[4],[5] mentioned above? Is the group fairness definition studied here different from [2] and [4]? Please also revise the related work.

Author response:
Concerning Weakness 3 and Question 2, we are grateful for your insights, and we have made modifications in the related work section, added discussion of comparison in Appendix E.1 and added discussion of the fairness metric in Appendix B.3.

More specifically, in Appendix B.3, we add:

>**Definition** B.7
A classifier h satisfies Bounded Group Loss (BGL) at level $\zeta$ under distribution $\mathcal{D}$ if for all $a \in A$, we have $\mathbb{E}[l(h(x), y) \mid A=a] \leq \zeta$.
>
>**Definition** B.8
    A classifier $h$ satisfies Conditional Bounded Group Loss (CBGL) for $y \in Y$ at level $\zeta_y$ under distribution $\mathcal{D}$ if for all $a \in A$, we have $\mathbb{E}[l(h(x), y) \mid A=a, Y=y] \leq \zeta_y$.
>
>When considering y as a binary variable and the loss function l being the 0-1 loss function, BGL is equivalent to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=a] \leq \zeta,$$
>holding for any a, whereas Demographic Parity refers to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=0]=\mathbb{P}[\hat{y}\neq y |\mid A=1].$$
>In this context, BGL can be understood as a relaxation of Demographic Parity.
>
>Similarly, when considering y as a binary variable and the loss function l being the 0-1 loss function, CBGL is equivalent to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=a, Y=y] \leq \zeta_y,$$
>holding for any a, whereas Equalized Odds refers to
>$$\mathbb{P}[\hat{y}\neq y |\mid A=0,Y=y]=\mathbb{P}[\hat{y}\neq y |\mid A=1,Y=y].$$
>In this context, CBGL can be understood as a relaxation of Equalized Odds.

Similarly, by **Definition** B.9，the metric that [2] considers can also be understood as a relaxation of Demographic Parity in the context of considering y as a binary variable and the loss function l being the 0-1 loss function. Due to the complexity of the formula, we do not present it here.

In Appendix E.1, we add:
>Differences between FedFaiREE and other fair federated learning methods lie in their approach to addressing fairness concerns. Many methods, akin to this paper, extend the principles of centralized machine learning to decentralized settings, such as FedFB[3], FedMinMax[2], PFFL[4], and others. These methods primarily focus on introducing fairness penalties in the objective functions and incorporate client reweighting schemes and terms (in objective functions) reweighting schemes that consider global or local fairness. The key divergence between our approach and these methods is that the latter typically converge and provide fairness guarantees only in large-sample scenarios, lacking assurances for fairness in small-sample situations, especially under distribution-free assumptions. Empirical results from Table 1 in this paper demonstrate that compared to FedFaiREE, methods like FedFB, FairFed[5] are not as effective in controlling fairness in small-sample scenarios. Furthermore, as these methods are predominantly in-processing techniques, while FedFaiREE falls under post-processing methods, there is a potential for further integration to achieve improved fairness guarantees as shown in our experiments. Moreover, another significant characteristic of FedFaiREE is its capability to adjust the trade-off between fairness and accuracy according to specific fairness constraints. This control capacity has been demonstrated in numerous experiments, showcasing an ability that other methods lack.

Reviewer comment (Experiments):
How does FedFaiREE perform for different fairness constraint parameter $\alpha$, different levels of data heterogeneity across clients and confidence level $\beta$? My understanding is that only $\alpha=0.1$ for the adult dataset, $\alpha=0.15$ for compas, and $\beta=0.95$ for all experiments.

Author response:
Regarding Question 3, we have conducted experiments with different fairness constraint parameters $\alpha$ and various confidence levels $\beta$ in Appendix C.3.

Table R1: Results with different $\alpha$ on Compas using FedAvg as input. $\overline{ACC}$ and $\overline{|DEOO|}$ represent the averages of accuracy and DEOO (defined in Equation (1) in the paper). Std ACC and Std DEOO represent standard deviations of accuracy and DEOO.  $|DEOO|_{95}$ represents the 95\% quantile of DEOO since we set the confidence level of FedFaiREE to 95\% in our experiments. 
|       $\alpha$        | 0.13   | 0.14   | 0.15   | 0.16   | 0.17   | 0.18   | FedAvg  |
|---------------|--------|--------|--------|--------|--------|--------|---------|
| $\overline{ACC}$ | 0.658  | 0.658  | 0.659  | 0.659  | 0.659  | 0.659  | 0.662   |
| Std ACC  | 0.011  | 0.010  | 0.010  | 0.011  | 0.011  | 0.011  | 0.011   |
| $\overline{\|DEOO\|}$     | 0.046  | 0.049  | 0.051  | 0.055  | 0.062  | 0.62  | 0.126   |
| Std DEOO      | 0.037  | 0.042  | 0.044  | 0.045  | 0.047  | 0.048  | 0.056   |
| $\|DEOO\|_{95}$       | 0.118  | 0.125  | 0.137  | 0.156  | 0.158  | 0.158  | 0.223   |

Table R2: Results with different $\beta$ on Compas using FedAvg as input. The notation meanings align with those in Table R1. 
|       $\beta$       | 0.01   | 0.03   | 0.05   | 0.07   | 0.09   | 0.11   | 0.13   | FedAvg |
|--------------|--------|--------|--------|--------|--------|--------|--------|--------|
| $\overline{ACC}$| 0.658  | 0.658  | 0.659  | 0.659  | 0.659  | 0.659  | 0.659  | 0.662  |
| Std ACC | 0.010  | 0.010  | 0.010  | 0.010  | 0.010  | 0.010  | 0.010  | 0.011  |
| $\overline{\|DEOO\|}$   | 0.046  | 0.050  | 0.051  | 0.052  | 0.053  | 0.055  | 0.055  | 0.126  |
| Std DEOO     | 0.038  | 0.042  | 0.044  | 0.045  | 0.045  | 0.044  | 0.045  | 0.056  |
| $\|DEOO\|_{95}$      | 0.118  | 0.125  | 0.137  | 0.148  | 0.156  | 0.156  | 0.156  | 0.223  |

Reviewer comment (Experiments):
What's the number of clients studied per dataset? If the number is low you should empirically examine how this approach scales for a larger number of clients. To illustrate that, you can consider for example the ACSIncome dataset, where the data are naturally noniid and partitioned into 50 states and Puerto Rico and treat each place as a client (i.e., 51 clients).

Author response:
Addressing Weakness 4, Question 4, and Question 5:
    1. Due to time constraints, we have only incorporated another dataset, ACSIncome. We will include more datasets, including CelebA, Bank, and Dutch as suggested in our final version.
    2. Thank you for your suggestion. We have updated Table 2 to include the number of clients,Table 4, 5 to reflect the standard deviations of metrics and added Table 6 to reflect standard deviations of metrics presented in Tables 1 (due to the space limitation). The sensitive group for each dataset is outlined in the dataset information in the EXPERIMENT Section. To enhance clarity, we have also included it in Table 2. The choice of the Dirichlet distribution parameter is based on available set segmentation requirements (ensuring a nonempty test set). We also conducted an experiment using a different Dirichlet distribution parameter on Adult as it shows in Appendix C.3.
    3. Although AFL is a client-fairness method, our intention in using it is for an initialization of our proposed FedFaiREE method. It serves as an input to demonstrate the broad applicability of FedFaiREE that it can take any method as input, and ensure the resulting classifier, after post-processing, satisfies fairness under the federated learning setting even with finite-sample.

Reviewer comment (Experiments):
It would be good for the authors to acknowledge the concerns regarding the COMPAS dataset within the fairness community.

Author response:
For “minor Question 1”, we have addressed this by including the following information in Appendix C.2:

>“We also note that there are concerns raised by the fairness community regarding the COMPAS dataset underscore crucial complexities within algorithmic fairness research. While Risk Assessment Instrument (RAI) datasets like COMPAS serve as prevalent benchmarks, their oversimplification of the intricate dynamics within real-world criminal justice processes poses significant challenges. Measurement biases and errors inherent in pretrial RAI datasets limit the direct translation of fairness claims to actual outcomes within the criminal justice system. Additionally, the technical focus on these data as a benchmark sometimes ignores the contextual grounding necessary for working with RAI datasets. Ethical reflection within socio-technical systems further highlights the necessity of acknowledging and grappling with the limitations and complexities inherent in RAI datasets.”

Reviewer comment (Presentation):
The margins around Figure 1 around the figure require editing. The main text touches on the figure's description.

Author response:
Thank you for your comment, we have revised this in our paper.