Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: This paper proposes an algorithm for multi-agent learning. By sharing gradient information but not data among agents, this algorithm can achieve collaborative learning while keeping data privacy. This paper also shows this algorithm can converge in limit, and provides supporting numerical experiments.
strengths_and_weaknesses: Strengths:

1. This algorithm solve the feature-distributed optimization problem, which is valuable and practical.

2. The algorithm shares gradient information rather than data, which preserves the privacy.

3. This paper proves the algorithm converges in limit.

4. The numerical experiments are comprehensive and convincing.

Weaknesses:

1. The theoretical analysis may be able to be improved. The current proof only shows convergence in limit, maybe can obtain an explicit convergence rate.
questions: N/A
limitations: N/A
review_title: null

final_score: rating: 5: Borderline accept: Technically solid paper where reasons to accept outweigh reasons to reject, e.g., limited evaluation. Please use sparingly.
review_content: summary: This paper studies collaborative learning between organizations without sharing data, models, and even objective functions. The authors propose a Gradient Assisted Learning (GAL) method that collaboratively optimize the aggregation of local loss functions, while each worker iteratively fits the gradients of the overarching objective function. The authors prove that the proposed GAL can achieve the performance close to centralized learning when all data, models, and objective functions are fully disclosed. The empirical results support this argument.
strengths_and_weaknesses: Pros:
+ This paper addresses a real and timely problem that collaboratively optimize the aggregation of local loss functions of different organizations, while each worker iteratively fits the gradients of the overarching objective functions.
+ The paper is well-written and easy to follow.

Cons:
- I find the setting is a bit unrealistic/confusing and needs further clarification. Is there any restrictions on the discrepancy between the data distributions of different organizations? In some extreme cases, would it be possible that the gradients from other devices cannot assist or have bad effects?
- The experiments are not comprehensive enough. Does any other existing method can be applied here. I understand that these methods are not designed to this setting and they may be rigorously reasonable, but I find this would be helpful to discuss them.
questions: Please response the cons in the rebuttal.
limitations: Limitations are duely discussed, while no potential negative societal impact is identified.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper proposes the gradient assistive learning (GAL) algorithm for a multi-organization collaborative learning setting. The proposed algorithm can be considered as a distributed version of the gradient boosting algorithm. The authors provide convergence analysis and experimental results to show the benefit of the proposed algorithm.
strengths_and_weaknesses: Strengths:

The paper studies an interesting distributed learning scenario which I think is relatively less explored in the community. The proposed algorithm is a reasonable extension of prior work on assistive learning. This paper also builds a connection between the GAL algorithm and the classic gradient boosting algorithm. The paper is relatively well-written and easy to follow. Authors provide results on a variety of datasets. I also appreciate that the authors provided detailed descriptions of the experiments in the appendix, as well as the results in the privacy enhancement setting. I did not check the proof details but the theoretical result looked sound.
Overall, I think this is a good paper.

Weaknesses:

For the MNIST and CIFAR experiments, authors assume that the images are split into patches, and different patches are stored in different organizations. I don't think this is a realistic experimental setup, and it's unclear why different organizations only store a patch of a natural image. I would like to see a more realistic scenario where this method can be applied in deep learning models.

questions: What would be a more realistic setting to apply this method in the image / language modality?
limitations: Authors mentioned that they "do not foresee any negative societal impacts". I agree with this.
review_title: null

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: Gradient Assisted Learning (GAL), a decentralized collaborative supervised learning methodology, is proposed. GAL works to assist a particular organization (dubbed "Alice") with their learning problem by utilizing other organization's learning capabilities but without sharing any sensitive information such as models or raw data between any two organizations. This is accomplished by iteratively having each "helper" organization learn a model of the (pseudo-)residual using their own data and sending the model's prediction back to Alice. At each iteration, Alice updates the overall model to be learned by incorporating these predictions and optimizing their associated weightings. At test-time, Alice queries the helper organizations for their predictions and feeds those predictions through the learned weightings to arrive at a prediction. A theorem is given that states that, under mild technical conditions, the loss of the GAL model converges (as the number of "assistance iterations" approaches infinity) to the optimal loss amongst all models in the span of the organizations' model function classes. Experiments on a variety of datasets shows that GAL outperforms prior state-of-the-art decentralized methods, with performance approaching that of centralized learning paradigms.
strengths_and_weaknesses: Strengths:

S1. Quality. The authors formulate their methodology into a technically sound and rigorous mathematical and algorithmic framework. The experiments are thorough and demonstrate the strengths of the proposed method in a wide range of applications.

S2. Clarity. The paper is well-motivated and easy to read.


Weaknesses:

W1. Originality. The proposed methodology is extremely similar to the prior "assisted learning" (AL) work [1], providing what seems to be only incremental extensions to the technical aspects of the AL framework. See below for more details regarding this concern.


Neutral:

N1. Significance. Since the proposed method (GAL) applies to more general problems (e.g., classification) than AL and is shown to outperform AL in the considered benchmarks, the reviewer finds that the paper advances the practical strength and performance of the literature's decentralized collaborative learning methods. However, the reviewer believes that the approaches taken by the authors to extend AL to GAL (i.e., replacing regression residuals with classification/regression pseudo-residuals, and optimizing the model weights and learning rates) are not significant conceptual/theoretical advancements.


[1] Xian, Xun, et al. "Assisted learning: A framework for multi-organization learning." Neurips 2020.
questions: Concerns/Suggestions:

CS1. Please explicitly tell the reader what the arrows in the tables represent.

CS2. The authors mention the "Interm" method in relation to Tables 1, 8, and 9, but this method does not appear in these tables.

CS3. In the proof of the theorem, when you multiplied both sides of (12) by $|\mu_{f^*}^g-\mu_{F^{t-1}}^g|$ and then summed up to arrive at (13), it appears to the reviewer that you forgot to multiply the $a_t^2$ term---shouldn't (13) have a factor of $\lVert\mu_{f^*}-\mu_{F^{t-1}}\rVert_1$ attached to the $a_t^2$ term? This may cause issues with the convergence of this term to zero as $t \to \infty$; you may need a condition requiring $a_t^2$ to go to zero faster than the partial sums of $a_t$ goes to $\infty$.

CS4. The authors state that "Prior work on AL is limited to mean squared loss regression with only two organizations using a sequential exchange of information." This doesn't appear to be entirely true---the original AL methodology appears applicable to regression tasks with $m\in\mathbb{N}$ organizations using arbitrary models and loss functions (not just mean squared loss), so long as the loss functions are minimized at the origin (e.g., they are norms). What is true is that the theoretical guarantee for converging to the oracle performance given in the AL paper is restricted to all $m$ organizations using linear regression models. It so happens that the pseudo-residual GAL uses, i.e., the functional gradient step, reduces to the conventional residual in the regression setting when Alice's loss function is equal to the squared error loss. With respect to this limitation, GAL generalizes past the regression task setting (allowing for classification settings) by sharing these pseudo-residuals (gradient values) instead of the residuals used in AL. Please fix these overstatements.

CS5. The reviewer is concerned with some of the claims made in Section 4.3 "Comparison with AL." In particular, the authors argue that GAL's enhanced performance over AL is in part due to the parallelizability (with respect to the organizations) of GAL, whereas AL is trained sequentially. However, AL appears to be immediately parallelizable as well; the `for` loop over organizations $j$ in Procedure 1 of the AL paper [1] does not depend on organization $j' \ne j$, so one may simply perform the AL model fitting for organization $j$ in parallel from the model fitting for the other organizations. With this in mind, GAL is not necessarily $M\times$ faster than AL as claimed in the paper, so the impact of the authors' Table 12 is limited to the contributions of "deep model sharing," which the authors claim is not always expected to help the performance of GAL.

CS6. Overall, the reviewer found the contributions to be overstated with respect to the earlier paper [1].


Questions:

Q1. Can you give an actual rate of convergence in your theorem instead of just an asymptotic result?


[1] Xian, Xun, et al. "Assisted learning: A framework for multi-organization learning." Neurips 2020.
limitations: The authors do a good job at describing their limitations, namely, that their theoretical convergence result is not sufficiently strong to describe why GAL converges after only a few iterations of "assistance," and that it may be possible to infer sensitive information from the shared pseudo-residuals. The authors demonstrate that upon adding moderate amounts of noise to the pseudo-residuals to take care of the latter limitation, the performance of GAL does not significantly deteriorate, and that performance is still better than that without collaboration. However, it would be interesting to discuss (and potentially test) the issue of adversarial sensitivity; what happens if one of the organizations is malicious or subject to an adversarial attack, either in their data or in the predictions that they feed back to Alice? Relative to traditional adversarial machine learning settings (wherein the adversary is typically assumed access to just one mode of attack at a time, e.g., the input data at test time), the proposed decentralized learning paradigm potentially offers an adversary more avenues for attack, e.g., at a particular organization's input data and at the various predictions fed to Alice.
review_title: null

Meta-review: metareview: This paper proposes and analyzes a new approach, Gradient Assisted Learning, to collaborative training when features are split across several organizations. Although the work has some limitations, all reviewers agreed that the paper contains a strong, sound contribution and should be accepted. The revisions made during the post-rebuttal discussion already address the majority of the concerns raised, which were about presentation of the results and clarifying some points wrt related work. 
confidence: Certain
Decision: Accept