# Papaya: Practical, Private, and Scalable Federated Learning

arXiv: https://arxiv.org/abs/2111.04877 · HTML: https://mlsanity.com/paper/2111.04877/

## Key facts

- Cited by 90 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 8 Nov 2021.

## Builds on (most cited first, 16 of 16)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima](https://mlsanity.com/paper/1609.04836/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Asynchronous Federated Optimization](https://mlsanity.com/paper/1903.03934/) (2019)
- [Federated Learning with Buffered Asynchronous Aggregation](https://mlsanity.com/paper/2106.06639/) (2021)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [Character-Aware Neural Language Models](https://mlsanity.com/paper/1508.06615/) (2015)
- [Asynchronous Federated Learning on Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2109.04269/) (2021)
- [Federated Evaluation and Tuning for On-Device Personalization: System Design & Applications](https://mlsanity.com/paper/2102.08503/) (2021)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [On Large-Cohort Training for Federated Learning](https://mlsanity.com/paper/2106.07820/) (2021)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [Secure Aggregation for Buffered Asynchronous Federated Learning](https://mlsanity.com/paper/2110.02177/) (2021)

## Built on by (most cited first, 30 of 90)

- [When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions](https://mlsanity.com/paper/2306.15546/) (2023)
- [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://mlsanity.com/paper/2204.05011/) (2022)
- [(Amplified) Banded Matrix Factorization: A unified approach to private training](https://mlsanity.com/paper/2306.08153/) (2023)
- [Federated Learning for Computationally-Constrained Heterogeneous Devices: A Survey](https://mlsanity.com/paper/2307.09182/) (2023)
- [On the Convergence of Federated Averaging with Cyclic Client Participation](https://mlsanity.com/paper/2302.03109/) (2023)
- [AFLGuard: Byzantine-robust Asynchronous Federated Learning](https://mlsanity.com/paper/2212.06325/) (2022)
- [TimelyFL: Heterogeneity-aware Asynchronous Federated Learning with Adaptive Partial Training](https://mlsanity.com/paper/2304.06947/) (2023)
- [CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning](https://mlsanity.com/paper/2210.02912/) (2022)
- [FS-Real: Towards Real-World Cross-Device Federated Learning](https://mlsanity.com/paper/2303.13363/) (2023)
- [FedShuffle: Recipes for Better Use of Local Work in Federated Learning](https://mlsanity.com/paper/2204.13169/) (2022)
- [Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneity](https://mlsanity.com/paper/2206.02633/) (2022)
- [FLINT: A Platform for Federated Learning Integration](https://mlsanity.com/paper/2302.12862/) (2023)
- [FedVS: Straggler-Resilient and Privacy-Preserving Vertical Federated Learning for Split Models](https://mlsanity.com/paper/2304.13407/) (2023)
- [Prompt Public Large Language Models to Synthesize Data for Private On-device Applications](https://mlsanity.com/paper/2404.04360/) (2024)
- [AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices](https://mlsanity.com/paper/2312.10935/) (2023)
- [Federated Learning in Practice: Reflections and Projections](https://mlsanity.com/paper/2410.08892/) (2024)
- [Optimizing the Communication-Accuracy Trade-off in Federated Learning with Rate-Distortion Theory](https://mlsanity.com/paper/2201.02664/) (2022)
- [The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscape](https://mlsanity.com/paper/2405.03636/) (2024)
- [Efficient and Light-Weight Federated Learning via Asynchronous Distributed Dropout](https://mlsanity.com/paper/2210.16105/) (2022)
- [Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare](https://mlsanity.com/paper/2405.06784/) (2024)
- [Recurrent Early Exits for Federated Learning with Heterogeneous Clients](https://mlsanity.com/paper/2405.14791/) (2024)
- [A Survey on Participant Selection for Federated Learning in Mobile Networks](https://mlsanity.com/paper/2207.03681/) (2022)
- [Federated Select: A Primitive for Communication- and Memory-Efficient Federated Learning](https://mlsanity.com/paper/2208.09432/) (2022)
- [Async-HFL: Efficient and Robust Asynchronous Federated Learning in Hierarchical IoT Networks](https://mlsanity.com/paper/2301.06646/) (2023)
- [Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation](https://mlsanity.com/paper/2308.00263/) (2023)
- [Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification](https://mlsanity.com/paper/2311.06141/) (2023)
- [DrJAX: Scalable and Differentiable MapReduce Primitives in JAX](https://mlsanity.com/paper/2403.07128/) (2024)
- [Noiseless Privacy-Preserving Decentralized Learning](https://mlsanity.com/paper/2404.09536/) (2024)
- [A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs](https://mlsanity.com/paper/2408.08868/) (2024)
- [Pisces: Efficient Federated Learning via Guided Asynchronous Training](https://mlsanity.com/paper/2206.09264/) (2022)

## Abstract (arXiv)

Cross-device Federated Learning (FL) is a distributed learning paradigm with several challenges that differentiate it from traditional distributed learning, variability in the system characteristics on each device, and millions of clients coordinating with a central server being primary ones. Most FL systems described in the literature are synchronous - they perform a synchronized aggregation of model updates from individual clients. Scaling synchronous FL is challenging since increasing the number of clients training in parallel leads to diminishing returns in training speed, analogous to large-batch training. Moreover, stragglers hinder synchronous FL training. In this work, we outline a production asynchronous FL system design. Our work tackles the aforementioned issues, sketches of some of the system design challenges and their solutions, and touches upon principles that emerged from building a production FL system for millions of clients. Empirically, we demonstrate that asynchronous FL converges faster than synchronous FL when training across nearly one hundred million devices. In particular, in high concurrency settings, asynchronous FL is 5x faster and has nearly 8x less communication overhead than synchronous FL.