# AFAFed -- Protocol analysis

arXiv: https://arxiv.org/abs/2206.14927 · HTML: https://mlsanity.com/paper/2206.14927/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 29 Jun 2022.

## Builds on (most cited first, 19 of 19)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Convex Optimization: Algorithms and Complexity](https://mlsanity.com/paper/1405.4980/) (2014)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [How to Escape Saddle Points Efficiently](https://mlsanity.com/paper/1703.00887/) (2017)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning for Wireless Communications: Motivation, Opportunities and Challenges](https://mlsanity.com/paper/1908.06847/) (2019)
- [Asynchronous Parallel Stochastic Gradient for Nonconvex Optimization](https://mlsanity.com/paper/1506.08272/) (2015)
- [Deep learning with Elastic Averaging SGD](https://mlsanity.com/paper/1412.6651/) (2014)
- [Deep Learning for IoT Big Data and Streaming Analytics: A Survey](https://mlsanity.com/paper/1712.04301/) (2017)
- [Staleness-aware Async-SGD for Distributed Deep Learning](https://mlsanity.com/paper/1511.05950/) (2015)
- [An Asynchronous Mini-Batch Algorithm for Regularized Stochastic Optimization](https://mlsanity.com/paper/1505.04824/) (2015)
- [Toward Understanding the Impact of Staleness in Distributed Machine Learning](https://mlsanity.com/paper/1810.03264/) (2018)
- [Proximity Without Consensus in Online Multi-Agent Optimization](https://mlsanity.com/paper/1606.05578/) (2016)
- [Multipath TCP: Analysis, Design and Implementation](https://mlsanity.com/paper/1308.3119/) (2013)
- [On the Convergence of Asynchronous Parallel Iteration with Unbounded Delays](https://mlsanity.com/paper/1612.04425/) (2016)
- [EcoMobiFog -- Design and Dynamic Optimization of a 5G Mobile-Fog-Cloud Multi-Tier Ecosystem for the Real-Time Distributed Execution of Stream Applications](https://mlsanity.com/paper/1906.07578/) (2019)

## Abstract (arXiv)

In this paper, we design, analyze the convergence properties and address the implementation aspects of AFAFed. This is a novel Asynchronous Fair Adaptive Federated learning framework for stream-oriented IoT application environments, which are featured by time-varying operating conditions, heterogeneous resource-limited devices (i.e., coworkers), non-i.i.d. local training data and unreliable communication links. The key new of AFAFed is the synergic co-design of: (i) two sets of adaptively tuned tolerance thresholds and fairness coefficients at the coworkers and central server, respectively; and, (ii) a distributed adaptive mechanism, which allows each coworker to adaptively tune own communication rate. The convergence properties of AFAFed under (possibly) non-convex loss functions is guaranteed by a set of new analytical bounds, which formally unveil the impact on the resulting AFAFed convergence rate of a number of Federated Learning (FL) parameters, like, first and second moments of the per-coworker number of consecutive model updates, data skewness, communication packet-loss probability, and maximum/minimum values of the (adaptively tuned) mixing coefficient used for model aggregation.