# CAFe: Cost and Age aware Federated Learning

arXiv: https://arxiv.org/abs/2405.15744 · HTML: https://mlsanity.com/paper/2405.15744/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 25 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 10 of 10)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [SAFA: a Semi-Asynchronous Protocol for Fast Federated Learning with Low Overhead](https://mlsanity.com/paper/1910.01355/) (2019)
- [Age of Information: An Introduction and Survey](https://mlsanity.com/paper/2007.08564/) (2020)
- [Age-Based Scheduling Policy for Federated Learning in Mobile Edge Networks](https://mlsanity.com/paper/1910.14648/) (2019)
- [Timely Communication in Federated Learning](https://mlsanity.com/paper/2012.15831/) (2020)
- [Version age-based client scheduling policy for federated learning](https://mlsanity.com/paper/2402.05407/) (2024)
- [Federated Learning Hyper-Parameter Tuning from a System Perspective](https://mlsanity.com/paper/2211.13656/) (2022)

## Abstract (arXiv)

In many federated learning (FL) models, a common strategy employed to ensure the progress in the training process, is to wait for at least M clients out of the total N clients to send back their local gradients based on a reporting deadline T, once the parameter server (PS) has broadcasted the global model. If enough clients do not report back within the deadline, the particular round is considered to be a failed round and the training round is restarted from scratch. If enough clients have responded back, the round is deemed successful and the local gradients of all the clients that responded back are used to update the global model. In either case, the clients that failed to report back an update within the deadline would have wasted their computational resources. Having a tighter deadline (small T) and waiting for a larger number of participating clients (large M) leads to a large number of failed rounds and therefore greater communication cost and computation resource wastage. However, having a larger T leads to longer round durations whereas smaller M may lead to noisy gradients. Therefore, there is a need to optimize the parameters M and T such that communication cost and the resource wastage is minimized while having an acceptable convergence rate. In this regard, we show that the average age of a client at the PS appears explicitly in the theoretical convergence bound, and therefore, can be used as a metric to quantify the convergence of the global model. We provide an analytical scheme to select the parameters M and T in this setting.