# Just-in-Time Aggregation for Federated Learning

**Authors:** K. R. Jayaram, Ashish Verma, Gegi Thomas, Vinod Muthusamy  
**arXiv:** [2208.09740](https://arxiv.org/abs/2208.09740)  
**Date:** 2022-08-20  
**Summary page:** https://www.summarizepaper.com/en/paper/2208.09740v1/

## Key points

- Increasing number and scale of federated learning (FL) jobs require efficient scheduling and management of aggregation
- Existing FL research has focused on FL algorithms and optimization techniques, neglecting the efficacy of aggregation
- Many FL platforms waste computational resources by employing aggregators that actively wait for model updates
- Proposed solution: "just-in-time" (JIT) aggregation to defer aggregation as much as possible, freeing up compute resources
- Novel approach to prioritize FL jobs for aggregation based on their specific requirements
- Experiments demonstrate that JIT aggregation can reduce resource usage by more than 60% compared to eager aggregation methods
- Implementing JIT aggregation has negligible overhead and minimal impact on the latency of FL jobs
- JIT aggregation optimizes resource utilization while maintaining low latency for FL jobs
- Potential benefits of adopting JIT aggregation in large-scale FL settings
- Contributes to advancing research on efficient scheduling and management strategies for federated learning systems.

## Summary

In the field of federated learning (FL), the increasing number and scale of FL jobs require efficient scheduling and management of aggregation to ensure the economic viability of cloud-hosted aggregation. However, existing FL research has primarily focused on designing FL algorithms and optimization techniques, neglecting the efficacy of aggregation. Many FL platforms currently employ aggregators that actively wait for model updates, resulting in wastage of computational resources in large-scale FL settings where parties are intermittently available for training. To address these challenges, this paper proposes a new FL aggregation paradigm called "just-in-time" (JIT) aggregation. The JIT aggregation leverages the unique properties of FL jobs, particularly the periodicity of model updates, to defer aggregation as much as possible. By doing so, it frees up compute resources for other FL jobs or datacenter workloads. The authors describe a novel approach to prioritize FL jobs for aggregation based on their specific requirements. To validate the effectiveness of JIT aggregation, the authors conduct experiments using multiple datasets, models, and FL aggregation algorithms. The results demonstrate that their techniques can significantly reduce resource usage by more than 60% compared to eager aggregation methods employed in existing FL platforms. Importantly, they also show that implementing JIT aggregation has negligible overhead and minimal impact on the latency of FL jobs. Overall, this paper introduces an innovative approach to address resource efficiency challenges in federated learning through JIT aggregation. By leveraging the periodicity of model updates and deferring aggregation until necessary, this technique optimizes resource utilization while maintaining low latency for FL jobs. The findings highlight the potential benefits of adopting JIT aggregation in large-scale FL settings and contribute to advancing research on efficient scheduling and management strategies for federated learning systems.