Weakness:
This work simply applies the meta-learning method into the federated learning setting. I can’t see any technical contribution, either in the meta-learning perspective or the federated perspective. The experimental results are not convincing because the data partition is not for federated learning. Reusing data partition in a meta-learning context is unrealistic for a federated learning setting.
The title is misleading or over-claimed. Only the adaptation phase costs a few rounds, but the communication cost of the meta-training phase is still high.
The non-IID partition is unrealistic. The authors simply reuse the dataset partitions used in the meta-learning context, which is not a real federated setting. Or in other words, the proposed method can only work in the distribution which is similar to the meta-learning setting.
Some meta earning-related benefits are intertwined with reducing communication costs. For example, the author claimed the proposed method has better generalization ability, however, this is from the contribution of the meta-learning. More importantly, this property can only be obvious when the data distribution cross-clients meet the assumption in the context of meta-learning.
The comparison is unfair to FedAvg. At least, we should let FedAvg use the same clients and dataset resources as those used in Meta-Training and Few-Rounds adaptation.
“Episodic training” is a term from meta-learning. I suggest the authors introduce meta-learning and its advantage first in the Introduction.
Few-shot FL-related works are not fully covered. Several recent published knowledge distillation-based few-shot FL should be discussed.
Overall Rating
I tend to clearly reject this paper because: 1) the proposed framework is a simple combination of meta-learning and federated learning. I cannot see any technical contribution. 2) Claiming the few round adaptations can reduce communication costs for federated learning is misleading, since the meta-training phase is also expensive. 3) the data partition is directly borrowed from meta-learning, which is unrealistic in federated learning.
---------after rebuttal--------
The rebuttal does not convince me with evidence, thus I keep my overall rating. I hope the author can obviously compare the total cost of meta-learning phase plus FL fine-tuning phase with other baselines.
1) the proposed framework is a simple combination of meta-learning and federated learning. I cannot see any technical contribution.