From: ICLR.cc/2023/Conference/Paper938/Authors
title: Submission Withdrawn by the Authors

From: ICLR.cc/2023/Conference/Paper938/Reviewer_od91
title: Acknowledgment
comment: I acknowledge and thank the authors for their clarifying comments.

From: ICLR.cc/2023/Conference/Paper938/Authors
title: Response to Reviewer od91
comment: We thank Reviewer od91 for the thoughtful comments. Below are our responses:

1. **There is a large body of of work on opportunistic networking where similar ideas were introduced.**
Opportunistic networks, or more specifically opportunistic relaying via client-client communication, certainly is not new in networking but how it affects asynchronous FL has never been studied. Our work offers the first principled investigation of the interplay between mobile relaying via client-client communication and the convergence of FL, and how to design the optimal relaying strategy to maximize the convergence speed. Both the problem and the proposed scheme/protocol are new, even in the area of networking. We believe the contribution of theoretically and empirically showing the benefit of mobile relays in the chaotic asynchronous setting is significant.


2. **The general conclusion of the paper is not surprising as relay surely expands the set of possibilities**
We agree with the reviewer that expanding the set of possibilities *may* improve the convergence performance, which *may* allow FedMobile to outperform ASYNC. However, to show that such benefits can actually be attained is not straightforward, especially in the chaotic asynchronous setting. We show, through both rigorous theoretical analysis and empirical studies, that using relays indeed improves convergence, and more importantly, how to design the optimal mobile FL system utilizing relays (i.e., when to relay and who to relay). 


3. **It is not clear for what use case anyone is/will be interested in this model**
One important application scenario of FL is smart public transportation [1], [2]. In the appendix, we described a use case of FedMobile in general VANET. Specifically, in smart public transportation, FedMobile can help forecast traffic flows and reduce public transportation customer waiting times. In particular, smart buses (typically IoV) collect traffic data and train local models, while roadside units (RSUs) aggregate the local models to produce a higher-accurate global model and broadcast the aggregated model to the buses. In this application scenario, the users (smart buses) have the incentive to apply FedMobile and the privacy concern becomes trivial.


4. **Some of the assumptions under which the main results are proven are restrictive and some are odd.**
  -  We do not think that bounded consecutive server meetings are restrictive in practical settings. If the server meeting times are unbounded, then essentially clients drop out and never return to the FL system and so we can disregard these clients. Similar assumptions on bounded time intervals have also been commonly applied in other asynchronous FL settings [3], [4]. 
 - The assumption that a client can meet at most one other client in each time slot is not restrictive at all. It is simply used for notational and analytical convenience. In fact, FedMobile works better if a client is able to meet more clients in each time slot because it gives the client more chances to find a good relay. 


[1] Xu, Chenhao, et al. "An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation." arXiv preprint arXiv:2208.07194 (2022).

[2] Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Blockchain em- powered asynchronous federated learning for secure data sharing in in- ternet of vehicles, IEEE Transactions on Vehicular Technology 69 (4) (2020) 4298–4311.

[3] D. Avdiukhin and S. Kasiviswanathan. "Federated learning under arbitrary communi- cation patterns". ICML, 2021.

[4] D. Basu, D. Data, C. Karakus, S. Diggavi. "Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations". NeurIPS, 2019.

From: ICLR.cc/2023/Conference/Paper938/Authors
title: Response to Reviewer VVF9 (part 2)
comment: 6. **I doubt this is novel, as this protocol is common in the area of networking**
We cannot agree with the reviewer on this point. Relaying via client-client communication certainly is not new in networking but how it affects asynchronous FL has never been studied. Our work offers the first principle investigation of the interplay between mobile relaying via client-client communication and the convergence of FL, and how to design the optimal relaying strategy to maximize the convergence speed. Both the problem and the proposed scheme/protocol are new, even in the area of networking. We believe the contribution of theoretically and empirically showing the benefit of mobile relays in the chaotic asynchronous setting is significant.

7. **Realistic of the scenario**
One important application scenario of FL is smart public transportation [7], [8]. In the appendix, we described a use case of FedMobile in general VANET. Specifically, in smart public transportation, FedMobile can help forecast traffic flows and reduce public transportation customer waiting times. In particular, smart buses (typically IoV) collect traffic data and train local models, while roadside units (RSUs) aggregate the local models to produce a higher-accurate global model and broadcast the aggregated model to the buses. In this application scenario, the users (smart buses) have the incentive to apply FedMobile and the privacy concern becomes trivial.


[1] Niknam, Solmaz, Harpreet S. Dhillon, and Jeffrey H. Reed. "Federated learning for wireless communications: Motivation, opportunities, and challenges." IEEE Communications Magazine 58.6 (2020): 46-51.

[2] Liu, Yi, et al. "A secure federated learning framework for 5G networks." IEEE Wireless Communications 27.4 (2020): 24-31.

[3] D. Avdiukhin and S. Kasiviswanathan. "Federated learning under arbitrary communi- cation patterns". ICML, 2021.

[4] Yang, H., Fang, M., and Liu, J. Achieving linear speedup with partial worker participation in non-{iid} federated learning. In International Conference on Learning Rep- resentations, 2021.

[5] Yu, Hao, Sen Yang, and Shenghuo Zhu. "Parallel restarted SGD with faster convergence and less communication: Demystifying why model averaging works for deep learning." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 33. No. 01. 2019.

[6] Reddi, Sashank J., et al. "Stochastic variance reduction for nonconvex optimization." International conference on machine learning. PMLR, 2016.

[7] Xu, Chenhao, et al. "An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation." arXiv preprint arXiv:2208.07194 (2022).

[8] Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Blockchain em- powered asynchronous federated learning for secure data sharing in in- ternet of vehicles, IEEE Transactions on Vehicular Technology 69 (4) (2020) 4298–4311.

From: ICLR.cc/2023/Conference/Paper938/Authors
title: Response to Reviewer VVF9 (part 1)
comment: We thank Reviewer VVF9 for the thoughtful comments. Below are our responses:

1. **It is really a wired scenario that uses the wireless network for training, which is not practical and unrealistic**
We do not catch the point raised by the reviewer. Client-server communication in FL is commonly studied in the wireless context (e.g., mobile phones communicate with base stations via 5G) [1][2]. In this paper, we consider the additional client-client communication, which is also common in 5G, IoT and vehicular networks, but is less considered in the FL problem. Using wireless network for the communication in FL training is widely accepted in the FL community. In the experiment, we simulate the wireless mobile networks scenario: at each time slot, we randomly sample $\rho N$ clients over total $N$ clients and randomly construct $\frac{\rho N}{2}$ client pairs. This simulates the random client-client meetings due to their mobility. We would much appreciate if the reviewer can further elaborate the question since we may have misunderstood it. 

2. **The main topic discussed in this work is the networking issue rather than a learning problem.**
We humbly disagree with the reviewer that our work is only a networking issue. Although we focus on a mobile scenario which seems like a network problem, the main propose of FedMobile is to investigate how to improve 'learning' performance under such mobile scenario. Networking and learning are complicatedly intertwined and cannot be studied separately. 

3. **What are the main challenges here?**
In Lemma1/Lemma2, we showed that the upload/download 'delay' depends on the upload/download relay search intervals. The main challenge is to make a rigorous connection between the upload/download search intervals and the ultimate the convergence performance, thereby guiding the relaying scheme design. In both theoretical and empirical results, we validated how to design the optimal mobile FL system utilizing relays (i.e., when to relay and who to relay). 

4. **Why does use the norm to quantify the function value in equations 24, 33, etc.?**
Using the norm to quantify the function value is a standard **first** step when proving the non-convex convergence in not only FL scenario [3], [4], [5] but also standard SGD [6].


5. **All the convergence curves seem not achieving a stable point yet. A sufficient and complete result should be provided.**
The convergence curves are close to the ultimate stable point. The reason why we didn't plot them for more time slots is because it could make it hard to read the figures and present our main idea. In fact, because FedMobile does not change the underlying optimization method of asynchronous FL and all clients still have the same amount of data, the final converged point of FedMobile is **expected to be similar** to the conventional ASYNC. Our point is that FedMobile converges **faster** than ASYNC by utilizing relays and different relaying schemes matter. Therefore, one should read the figures by fixing a target accuracy level and comparing the number of time slots needed to reach that level.

From: ICLR.cc/2023/Conference/Paper938/Authors
title: Response to Reviewer QnJj (part 2)
comment: 5. **FedMobile does not seem to outperform other baselines significantly**
We humbly disagree with the reviewer that the improvement of FedMobile is small. In Figure 4., we emphasize that the required times slots by using FedMobile are decreased by 17.5%-38.2% on FMNIST and CIFAR-10 datasets. Note that the fastest convergence is achieved by the mechanism where clients communicate with the server in each time slot. We list the time need to reach different accuracy targets in FMNIST including the aforementioned lower bound to validate the improvement of FedMobile. As we can see, FedMobile significantly outperforms ASYNC and achieves close convergence speed to the ideal Lower Bound. 

	- FMNIST Results with fixed communication intervals:
	|      | Test Acc achieves 0.6 | Test Acc achieves 0.7 |
	|------|-----|-----|
	| ASYNC   | 113   |  178 |
	| FedMobile   | 82   |  115  |
	| FedMobile via Multiple Relays | 61 |  102 |
	| Lower Bound | 44| 69|

6. **The scale of the experiment is small**
In most FL works, especially for the asynchronous scenario, FMNIST and CIFAR10 are curated from previous FL papers and widely used as benchmarks in FL studies [5], [10], [11], [12]. Here we add one more experiment on the Non-IID Shakespeare datasets which utilizes CharLSTM as the backbone model. The following table reports the time slots needed to reach different accuracy targets for ASYNC and FedMobile:

	- Shakespeare Results with fixed communication intervals:
	|      | Test Acc achieves 0.3 | Test Acc achieves 0.4 |
	|------|-----|-----|
	| ASYNC   | 82   |  267 |
	| FedMobile   | 61   |  158  |
	| Required Time Slots Decreased By | 25.6%  |  40.8% |



[1] Arash Asadi, Qing Wang, and Vincenzo Mancuso. A survey on device-to-device communication in cellular networks. IEEE Communications Surveys & Tutorials, 16(4):1801–1819, 2014.

[2] Zhe Yu, Yanmin Gong, Shimin Gong, and Yuanxiong Guo. Joint task offloading and resource allocation in UAV-enabled mobile edge computing. IEEE Internet of Things Journal, 7(4):3147– 3159, 2020.

[3] Guo, Yuanxiong, et al. "Hybrid Local SGD for Federated Learning with Heterogeneous Communications." International Conference on Learning Representations. 2021.

[4] Lin, Frank Po-Chen, et al. "Semi-decentralized federated learning with cooperative D2D local model aggregations." IEEE Journal on Selected Areas in Communications 39.12 (2021): 3851-3869.

[5] D. Avdiukhin and S. Kasiviswanathan. "Federated learning under arbitrary communi- cation patterns". ICML, 2021.

[6] Yang, Haibo, et al. "Anarchic federated learning." International Conference on Machine Learning. PMLR, 2022.

[7] D. Basu, D. Data, C. Karakus, S. Diggavi. "Qsparse-local-sgd: Distributed sgd with quantization, sparsification and local computations". NeurIPS, 2019.

[8] Xu, Chenhao, et al. "An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation." arXiv preprint arXiv:2208.07194 (2022).

[9] Y. Lu, X. Huang, K. Zhang, S. Maharjan, Y. Zhang, Blockchain em- powered asynchronous federated learning for secure data sharing in in- ternet of vehicles, IEEE Transactions on Vehicular Technology 69 (4) (2020) 4298–4311.

[10] McMahan, H. B., Moore, E., Ramage, D., Hampson, S., et al. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629, 2016.

[11] Li, T., Sahu, A. K., Zaheer, M., Sanjabi, M., Talwalkar, A., and Smith, V. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127, 2018.

[12] Yang, H., Fang, M., and Liu, J. Achieving linear speedup with partial worker participation in non-{iid} federated learning. In International Conference on Learning Rep- resentations, 2021.

From: ICLR.cc/2023/Conference/Paper938/Authors
title: Response to Reviewer QnJj (part 1)
comment: We thank Reviewer QnJj for the thoughtful comments. Below are our responses:

1.  **Isn’t client-client communication harder/slower than client-server communication?**
In modern communication networks, client-client communication could be much faster than client-server communication. For instance, [1], [2] have shown that in 5G-and-beyond mobile networks, the device can communicate with its nearby devices via device-to-device links at high date rates. In addition, the works in FL that utilize the client-client communication to improve the system convergence performance, raise great interests in the past few years (e.g. [3], [4]). However, existing works [3], [4] focus on the fixed topology and synchronous aggregation scenario, which is much different from our mobile and asynchronous setting. 

2. **What if the "right" client to relay is not accessible?**
If there is no qualified ("right") client to relay, the client will transmit its CLU to the server by itself. The assumption of having at least one semi-qualified relay in each search interval is discussed in Theorem 1. In particular, Proposition 2 gives a specific form of the probability that this assumption holds under a simple mobility model. The point that we tried to make is that with a larger $\rho$ (i.e., more client-client meeting opportunities), this assumption holds with a larger probability. 

3. **Can't the server simply reject clients' updates with high staleness**
Under the assumption that clients know their next server meeting time, FedMobile by design ensures that uploads via relaying will never be late. Further, the assumption that each client can communicate with the server at least one time in each $\Delta$ time slots, bounds the highest staleness. Note that an upper bound of highest staleness is a common assumption which has also been made in many recent asynchronous FL works (e.g. [5], [6], [7]). Same as in these existing works, we did not consider rejecting clients' update with high staleness but this can be an interesting mechanism to explore in the future. 

4. **Concrete application scenario for FedMobile**
One important application scenario of FL is smart public transportation [8], [9]. In the appendix, we described a use case of FedMobile in general VANET. Specifically, in smart public transportation, FedMobile can help forecast traffic flows and reduce public transportation customer waiting times. In particular, smart buses (typically IoV) collect traffic data and train local models, while roadside units (RSUs) aggregate the local models to produce a higher-accurate global model and broadcast the aggregated model to the buses. In this application scenario, the users (smart buses) have the incentive to apply FedMobile and the privacy concern becomes trivial.

From: ICLR.cc/2023/Conference/Paper938/Reviewer_QnJj
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: The paper proposes FedMobile, an asynchronous federated learning framework that leverages random client-to-client communication to speed up the convergence of federated learning. The method is motivated by an intuition that in asynchronous federated learning, clients' updates may not get a chance to return to the server in a timely manner, thus the federated model may not guarantee to converge. FedMobile thus leverages client-client relay to mitigate such an issue. Both theoretical analysis and empirical results are shown to justify the effectiveness of the proposed FedMobile method.
strength_and_weaknesses: Strength:  
- The paper is well-written in general.  
- Improving the convergence of asynchronous federated learning is a research direction of good potential importance.  
- Both theoretical analysis and experimental results are shown to justify the proposed method.  

Weaknesses:  
- Coming up with a method that enjoys rigorous theoretical guarantees is great. However, my major concern is that the application scenarios for FedMobile are not super clear. Isn't client-client communication harder/slower than client-server communication? What if the "right" client to relay is not accessible? On another aspect, can't the server simply reject clients' updates with high staleness, which I believe is a common practice in asynchronous optimization? Overall, I highly encourage the authors to elaborate on a few concrete application scenarios for FedMobile. The current motivations look somewhat artificial.  
- Based on the major experimental results, e.g., the ones in Figure 3 - 6, FedMobile does not seem to outperform other baselines significantly. If that is true, why would people want to use FedMobile in practice given it potentially harms the clients' privacy?  
- The scale of the experiment is too small. What happens for the ImageNet scale experiments? And how does the performance look for Transformer models?  
clarity,_quality,_novelty_and_reproducibility: - Clarity: The paper's clarity is good and the method is easy to follow.  
- Quality: The quality of the paper is fair given its good writing quality and theoretical and experimental results.  
- Novelty: The proposed method is somewhat novel, which enables client-client relay in federated learning scenarios.  
- Reproducibility: Enough experimental setups and details are provided, I thus believe the experimental results can be reproduced.  
summary_of_the_review: The paper proposes FedMobile which enables client-to-client communication to enhance the convergence of asynchronous federated learning. Though both theoretical analysis and experimental results are shown to justify FedMobile, the method's motivation does not seem very convincing. Moreover, FedMobile does not seem to provide significant speedups in practice either. I thus tend to question the necessity of the proposed method.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 3: reject, not good enough

From: ICLR.cc/2023/Conference/Paper938/Reviewer_VVF9
confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
summary_of_the_paper: This paper considers an asynchronous federated learning setting by leveraging opportunistic mobile relaying, which shows that the random client-client communication pattern in a mobile network is able to create additional indirect communication opportunities so that the training phase can be accelerated. In this work, it is also shown that the convergence rate of the proposed FedMobile is at the order of $1/\sqrt{NT}$. Multiple numerical results illustrate the proposed FedMobile can converge faster than the state-of-the-art methods.
strength_and_weaknesses: The major strength of this work is that FedMobile considers the relay issue in the FL training process. As there would be more client-server communications explored, it would be beneficial for the asynchronous federated learning systems to achieve a faster convergence rate. Theoretically, it has been shown that FedMobile can find the first-order stationary points with provable convergence rate guarantees. 

The major concerns of this work are as follows:

1) it is really a wired scenario that uses the wireless network for training, which is not practical and unrealistic. Please clarify which system requests this kind of training. Note that asynchronous federated learning systems are built upon large-scale clusters, which considers quite different systems here.

2) There is a motivating example (VANET) shown in the appendix, but it seems that all the experiments are not based on this scenario. The main topic discussed in this work is the networking issue rather than a learning problem.

3) the discussed protocol is straightforward under the condition that $\Delta$ is bounded. From lemmas 1 and 2, it can be clear that the relay model in terms of delay is linear. What are the main challenges here?

4) The theoretical proof is also standard, and there are some issues, e.g., why does use the norm to quantify the function value in equations 24, 33, etc.?

5) all the convergence curves seem not achieving a stable point yet. A sufficient and complete result should be provided.


clarity,_quality,_novelty_and_reproducibility: Clarity: The paper generally well written, but does not present the full algorithm in a clear way.
Quality: it is encouraged to argue the main challenges of the showing the convergence of the proposed FedMobile. Please do not hide the dependence of the model parameters in the convergence result, e.g., write down the expression of C and D. Also, as the bounded gradient is assumed, it is not surprising that the algorithm can converge even the delay would be large.
Novelty: I doubt this is novel, as this protocol is common in the area of networking. 
Reproducibility: the original code is not submitted, but only some results. But as the VANET is not used, from the current description of the toy model, it is easy to reproduce the result.

summary_of_the_review: In summary, this work considers the possibility of using relaying for accelerating the training performance of FL systems, but there are many technical concerns, e.g., realistic of the scenario, quality of the convergence results, clearness of the algorithm presentation.
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
empirical_novelty_and_significance: Not applicable
flag_for_ethics_review: NO.
recommendation: 3: reject, not good enough

From: ICLR.cc/2023/Conference/Paper938/Reviewer_od91
confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
summary_of_the_paper: The authors study Federated Learning when clients send and receive updates to a server as opportunity arises and in an asynchronous manner. Clients can also use each other as relay, with up to 1 upload/download per client.  Convergence is shown to scale like 1/sqrt(NT) where N is the number of clients and T is the number of communication rounds. A set of results are proven about the proposed scheme, its convergence, under a set of assumptions.  Numerical results are provided for MNIST and CIFAR10 and some synthetic data comparing performance with other recently proposed asynchronous schemes.
strength_and_weaknesses: 
Strengths

o A novel asynchronous scheme is proposed which allows for relay

o Theoretical results for convergence are proved

o Performance charts are provided

Weaknesses

o There is a large body of of work on opportunistic networking where similar ideas were introduced 

o The general conclusion of the paper is not surprising as relay surely expands the set of possibilities

o The proof of convergence is provided under strong assumptions on mobility patters

o It is not clear for what use case anyone is/will be interested in this model
clarity,_quality,_novelty_and_reproducibility: 
The paper is generally well written and easy to follow.  It contains a theoretical framework with proofs and an algorithm with performance guarantees.  The novelty of the proposed framework is limited as similar ideas were developed and used 15 years ago or so in data networking.  Some of the assumptions under which the main results are proven are restrictive and some are odd.  Eg, "we assume that the time interval between any two consecutive server meetings for any client is bounded by Δ" and then this Δ is used to tune the search interval.  How do we estimate this parameter so that search intervals are not overly long?  Similarly how does one estimate T (the number of optimization steps) on which the convergence sensitively depends?  Or why just one relay per client?  As far as reproducibility is concerned, I'm not certain how this is to be done but this is not a central issue.

summary_of_the_review: 
The paper proposes asynchronous federated learning via mobile clients with a single relay for upload and download.  Convergence is shown theoretically under assumptions some of which are restrictive and some are for convenience.  Eg, there is interplay between key parameters of the algorithm (Δ and T) and its performance and yet no recipe is provided how to set or tune these parameters in case someone decides to use this model.  No clear use case for the proposed framework is provided.  
correctness: 3: Some of the paper’s claims have minor issues. A few statements are not well-supported, or require small changes to be made correct.
technical_novelty_and_significance: 3: The contributions are significant and somewhat new. Aspects of the contributions exist in prior work.
empirical_novelty_and_significance: 2: The contributions are only marginally significant or novel.
flag_for_ethics_review: NO.
recommendation: 6: marginally above the acceptance threshold