TiFL: A Tier-based Federated Learning System (2001.09249v1)

Abstract: 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.

Tier-based Federated Learning System: Tackling Heterogeneity in Federated Learning

The paper "TiFL: A Tier-based Federated Learning System" presents an innovative approach to addressing key challenges in Federated Learning (FL), specifically those posed by resource and data heterogeneity among participating clients. FL, as a decentralized method, allows for the training of machine learning models across a vast number of clients without necessitating centralized data aggregation, thereby preserving privacy in compliance with regulations such as the GDPR and HIPAA. However, this setup inherently introduces variability in client resources and data distributions, leading to potential inefficiencies in training performance and model accuracy.

Key Concepts and System Design

The authors propose TiFL, a federated learning framework that introduces a "tier-based" client selection strategy to mitigate the adverse effects of heterogeneity. Their approach involves segmenting clients into tiers based on their training performance. These tiers are used to inform the selection of clients for each round of training, minimizing the impact of stragglers—clients with lower computational resources or challenging data distributions that slow down the training process.

1. Profiling and Tiering: TiFL begins by profiling clients to assess their training latency, subsequently grouping them into tiers. Each client’s training performance is measured and updated over time, allowing for dynamic tiering that adapts to changes in client conditions.
2. Static Tier Selection: The authors explore several static selection strategies that assign predefined probabilities for sampling clients from each tier. While this approach can yield improvements in training time, it comes with risks related to potential training bias due to non-representative client sampling.
3. Adaptive Tier Selection: To better balance training time and model accuracy, TiFL employs an adaptive client selection algorithm. This approach adjusts tier selection probabilities based on observed accuracy metrics, seeking to include underrepresented data distributions as needed while maintaining efficient training.

Empirical Evaluation and Results

The paper provides an exhaustive empirical assessment of TiFL using both simulated environments and the LEAF benchmark framework, which models realistic FL scenarios. Across varied conditions of heterogeneity—including resource, data quantity, and non-IID data distributions—TiFL demonstrates substantial improvements:

• Training Time: In environments with resource heterogeneity, TiFL achieves significant speed-ups, notably a 6× reduction in training time when using faster tiers more frequently. Even under data quantity heterogeneity, training times saw a 3× improvement.
• Model Accuracy: Despite prioritizing faster-tier clients, TiFL maintains competitive accuracy compared to conventional FL approaches, largely due to its adaptive strategy addressing potential biases in data distribution.

The results clearly suggest that adaptive tier selection can offer both efficiency and effectiveness in federated learning environments where client heterogeneity is a critical factor.

Implications and Future Work

Practically, TiFL enriches the potential application domains of federated learning by offering a scalable solution for heterogeneous client settings typical in mobile and IoT contexts. Theoretically, it expands on existing FL methodologies by integrating adaptive mechanisms responsive to data distributional changes without compromising client privacy or security.

Looking forward, the approach proposed in TiFL raises interesting directions for future research. Potential improvements could involve further refinement of adaptive algorithms to better handle abrupt changes in client conditions, integration with privacy-preserving techniques at scale, and extensions for cross-device learning in particularly challenging environments such as those affected by considerable network instability or data sparsity.