# Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT

arXiv: https://arxiv.org/abs/2108.07504 · HTML: https://mlsanity.com/paper/2108.07504/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 17 Aug 2021.

## Builds on (most cited first, 9 of 9)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning for Channel Estimation in Conventional and RIS-Assisted Massive MIMO](https://mlsanity.com/paper/2008.10846/) (2020)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [Federated Learning for Task and Resource Allocation in Wireless High Altitude Balloon Networks](https://mlsanity.com/paper/2003.09375/) (2020)
- [Dynamic Task Offloading and Resource Allocation for Ultra-Reliable Low-Latency Edge Computing](https://mlsanity.com/paper/1812.08076/) (2018)
- [Optimized Age of Information Tail for Ultra-Reliable Low-Latency Communications in Vehicular Networks](https://mlsanity.com/paper/1912.07981/) (2019)
- [Taming the Tail of Maximal Information Age in Wireless Industrial Networks](https://mlsanity.com/paper/1909.04335/) (2019)
- [Minimizing Age of Information for Real-Time Monitoring in Resource-Constrained Industrial IoT Networks](https://mlsanity.com/paper/1912.07186/) (2019)
- [Age-Optimal Power Allocation in Industrial IoT: A Risk-Sensitive Federated Learning Approach](https://mlsanity.com/paper/2012.06860/) (2020)

## Abstract (arXiv)

While information delivery in industrial Internet of things demands reliability and latency guarantees, the freshness of the controller's available information, measured by the age of information (AoI), is paramount for high-performing industrial automation. The problem in this work is cast as a sensor's transmit power minimization subject to the peak-AoI requirement and a probabilistic constraint on queuing latency. We further characterize the tail behavior of the latency by a generalized Pareto distribution (GPD) for solving the power allocation problem through Lyapunov optimization. As each sensor utilizes its own data to locally train the GPD model, we incorporate federated learning and propose a local-model selection approach which accounts for correlation among the sensor's training data. Numerical results show the tradeoff between the transmit power, peak AoI, and delay's tail distribution. Furthermore, we verify the superiority of the proposed correlation-aware approach for selecting the local models in federated learning over an existing baseline.