TiFL: A Tier-based Federated Learning System
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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.
Key facts. Cited by 96 papers in the map (data through 14 Oct 2026). First posted on arXiv 25 Jan 2020.
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Citing papers per year: 2021: 22; 2022: 12; 2023: 22; 2024: 16; 2025: 18; 2026: 6.
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50 newest of 96 All 96 (JSON)
- 2021Towards Personalized Federated Learningcited by 464
- 2023Heterogeneous Federated Learning: State-of-the-art and Research Challengescited by 161
- 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
- 2022pFL-Bench: A Comprehensive Benchmark for Personalized Federated Learningcited by 44
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
- 2023Efficient Personalized Federated Learning via Sparse Model-Adaptationcited by 38
- 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
- 2021EasyFL: A Low-code Federated Learning Platform For Dummiescited by 20
- 2021Architectural Patterns for the Design of Federated Learning Systemscited by 17
- 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
- 2021Scalable federated machine learning with FEDncited by 16
12 most cited of 93 All 93 (JSON)
- 2026Latency-Aware Client Assignment for Parallel Split Learning With Global Sampling26 Sep 2026
- 2026Concurrent Split Learning Through Stable Client Clustering24 Sep 2026
- 2026Task-oriented Framework for Communication-Efficient Federated Learning: From Isolated Optimization to Holistic Synergy4 Sep 2026
- 2026Beyond Non-IID: Learner--Client Distribution Mismatch in Federated Learning27 Aug 2026
- 2026EvoCSFL: Surrogate-Assisted Evolutionary Client Selection for Efficient and Robust Federated Learning5 Jun 2026
- 2026Hardware-Aware Federated Learning for Speech Emotion Recognition23 May 2026
- 2025Personalized Collaborative Learning with Affinity-Based Variance Reduction17 Oct 2025
- 2025Hierarchical Federated Learning for Social Network with Mobility18 Sep 2025
- 2025FedSSG: Expectation-Gated and History-Aware Drift Alignment for Federated Learning17 Sep 2025
- 2025Green Federated Learning via Carbon-Aware Client and Time Slot Scheduling10 Sep 2025
- 2025Semi-decentralized Federated Time Series Prediction with Client Availability Budgets3 Sep 2025
- 2025Optimizing Federated Learning for Scalable Power-demand Forecasting in Microgrids11 Aug 2025
12 newest of 93 All 93 (JSON)