Venue: NeurIPS 2022 Workshop Federated_Learning

Reviews:

final_score: rating: null
review_content: Pros.

1. This paper propose a way to estimate the average over all clients under intermittent connection. Convergence upper bounds are derived for the proposed communication pattern.

Cons.

1. The proposed ColRel is very similar to that in [6] so it is not new. The only difference is that clients with stable connection work like a local server.

2.  The paper seems not considering multiple local updates, which is the most important feature of federated learning compared with distributed learning.

3. Lemma 3.1 says the chosen $\alpha$ should bring an unbiased estimation, which is strong.

Q1. Can any given $p_i$ and $p_{ij}$ allow to find such an $\alpha$? what is the restriction for $p_i$ and $p_{ij}$?

Q2. If the number of local updates is large, how about this condition and how to minimize the variance?

Summary.

This paper proposed a way to optimize $\alpha$ such that with an unbiased estimation for the averaged gradients, the variance is minimized. However, this paper does not consider multiple local updates. The framework is not new compared to previous works. 
review_title: A new way to estimate the average over all clients under intermittent connection

final_score: rating: null
review_content: The paper proposes an interesting, seemingly novel strategy for making FL more robust in edge devices, where each client sends not only its own update but an aggregate of neighboring clients' updates. It is well-written; nicely motivated, providing a good explanation of why such a strategy is needed; and results are presented clearly.

The strategy is derived by looking at the distributed mean estimation problem, and its consequences to convex optimizations are then assessed. Numerical experiments are performed to show how it can be useful in different settings.

A discussion around privacy considerations is missing. Could for instance the clients perform secure aggregation? This is an important point in federated learning and should not be ignored.

Overall, it seems like the proposed algorithm can be quite useful in edge scenarios, even though its practical implementation can be tricky (how to have clients securely communicating etc.).

Minor points:
- the way ColRel oscillates in the numerical experiments should be better explained
- it is not clear to me what is precisely the difference between blind and non-blind FedAvg; how come the blind approach does not fail in the non-iid case?
review_title: Review for "ColRel: Collaborative Relaying for Federated Learning over Intermittently Connected Networks"

final_score: rating: null
review_content: This paper proposes a collaborative relaying (ColRel) system that aims to improve the convergence of FL systems when clients have intermittent connectivity to the central server. Clients participating in the relaying system exchange and aggregate model updates with neighbours before sending them to the central server. When clients have more reliable connections to neighbours than the central server, as may be the case in IoT networks, ColRel can more reliably include information from all clients.

Quality:
The work presents a clear motivation and method, with convergence analysis for some types of loss functions, and some experimental justification. 

Clarity:
The paper is well written and presented in a logical manner.

Originality:
To my knowledge the collaborative relaying framework and convergence analysis is original.

Significance:
The ideas will be interesting to the FL research community.

Pros:
 - When clients have more reliable connections to neighbours than to the central server, their model updates can be more consistently included.
 - The mathematical framework is laid out clearly, and the theorems are interesting (I have not verified the proofs).

Cons:
 - The privacy of data is reduced. Model updates are widely shared among clients rather than a single (trusted) central server.
 - The overall communication used is increased by a potentially large multiple.
 - Clients which are already struggling to communicate their updates are now required to communicate their updates many times, adding resource pressure.
 - Clients must receive and aggregate multiple model updates which significantly increases the computational burden (especially memory) on edge devices which are likely to be resource constrained.

I would encourage the authors to address the increase in resources quantitatively in the future, and to think about how the additional privacy exposure of model updates to (potentially unfriendly) neighbours could be mitigated, for example with differential privacy or proxy models [Shen et al. "Federated Mutual Learning"; Kalra et al. "ProxyFL: Decentralized Federated Learning through Proxy Model Sharing"].

I think the convex relaxation in eq. 5 should be further justified and discussed both theoretically and empirically. It also seems unrealistic that the probabilities $p_i$ and $p_{ij}$ would be known in practice, which is a prerequisite for finding $\alpha_{ij}$. It would be good to discuss and experimentally show how these probabilities can be determined when a network starts up, and how they can be updated as the connectivities evolve online.

Possible typo in line 122 $P(\tau_{ij} | \tau_{ij})$.

review_title: Collaborative Relaying can improve FL systems when clients have intermittent connectivity

Decision: Accept (Poster)