# TiFL: A Tier-based Federated Learning System

arXiv: https://arxiv.org/abs/2001.09249 · HTML: https://mlsanity.com/paper/2001.09249/

## Key facts

- Cited by 96 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

## Built on by (most cited first, 30 of 96)

- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Heterogeneous Federated Learning: State-of-the-art and Research Challenges](https://mlsanity.com/paper/2307.10616/) (2023)
- [Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Sampling](https://mlsanity.com/paper/2112.11256/) (2021)
- [pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learning](https://mlsanity.com/paper/2206.03655/) (2022)
- [A Survey on Heterogeneous Federated Learning](https://mlsanity.com/paper/2210.04505/) (2022)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [Efficient Personalized Federated Learning via Sparse Model-Adaptation](https://mlsanity.com/paper/2305.02776/) (2023)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning](https://mlsanity.com/paper/2102.00655/) (2021)
- [EasyFL: A Low-code Federated Learning Platform For Dummies](https://mlsanity.com/paper/2105.07603/) (2021)
- [Architectural Patterns for the Design of Federated Learning Systems](https://mlsanity.com/paper/2101.02373/) (2021)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Scalable federated machine learning with FEDn](https://mlsanity.com/paper/2103.00148/) (2021)
- [FLASHE: Additively Symmetric Homomorphic Encryption for Cross-Silo Federated Learning](https://mlsanity.com/paper/2109.00675/) (2021)
- [Asynchronous Federated Learning for Sensor Data with Concept Drift](https://mlsanity.com/paper/2109.00151/) (2021)
- [FLoRA: Single-shot Hyper-parameter Optimization for Federated Learning](https://mlsanity.com/paper/2112.08524/) (2021)
- [FedHe: Heterogeneous Models and Communication-Efficient Federated Learning](https://mlsanity.com/paper/2110.09910/) (2021)
- [FLGo: A Fully Customizable Federated Learning Platform](https://mlsanity.com/paper/2306.12079/) (2023)
- [Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settings](https://mlsanity.com/paper/2102.02849/) (2021)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Time-triggered Federated Learning over Wireless Networks](https://mlsanity.com/paper/2204.12426/) (2022)
- [Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift](https://mlsanity.com/paper/2211.12578/) (2022)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategies](https://mlsanity.com/paper/2109.03999/) (2021)
- [MAS: Towards Resource-Efficient Federated Multiple-Task Learning](https://mlsanity.com/paper/2307.11285/) (2023)
- [Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2404.13804/) (2024)
- [FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization](https://mlsanity.com/paper/2405.06312/) (2024)
- [Ravan: Multi-Head Low-Rank Adaptation for Federated Fine-Tuning](https://mlsanity.com/paper/2506.05568/) (2025)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)
- [A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications](https://mlsanity.com/paper/2403.01387/) (2024)

## Abstract (arXiv)

Federated Learning (FL) enables learning a shared model across many clients without violating the privacy requirements. One of the key attributes in FL is the heterogeneity that exists in both resource and data due to the differences in computation and communication capacity, as well as the quantity and content of data among different clients. We conduct a case study to show that heterogeneity in resource and data has a significant impact on training time and model accuracy in conventional FL systems. To this end, we propose TiFL, a Tier-based Federated Learning System, which divides clients into tiers based on their training performance and selects clients from the same tier in each training round to mitigate the straggler problem caused by heterogeneity in resource and data quantity. To further tame the heterogeneity caused by non-IID (Independent and Identical Distribution) data and resources, TiFL employs an adaptive tier selection approach to update the tiering on-the-fly based on the observed training performance and accuracy overtime. We prototype TiFL in a FL testbed following Google's FL architecture and evaluate it using popular benchmarks and the state-of-the-art FL benchmark LEAF. Experimental evaluation shows that TiFL outperforms the conventional FL in various heterogeneous conditions. With the proposed adaptive tier selection policy, we demonstrate that TiFL achieves much faster training performance while keeping the same (and in some cases - better) test accuracy across the board.