# TablePuppet: A Generic Framework for Relational Federated Learning

arXiv: https://arxiv.org/abs/2403.15839 · HTML: https://mlsanity.com/paper/2403.15839/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 51 references that are in the map.
- First posted on arXiv 23 Mar 2024.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
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- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Federated Optimization: Distributed Machine Learning for On-Device Intelligence](https://mlsanity.com/paper/1610.02527/) (2016)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Multitask learning and benchmarking with clinical time series data](https://mlsanity.com/paper/1703.07771/) (2017)
- [FedHealth: A Federated Transfer Learning Framework for Wearable Healthcare](https://mlsanity.com/paper/1907.09173/) (2019)
- [SecureBoost: A Lossless Federated Learning Framework](https://mlsanity.com/paper/1901.08755/) (2019)
- [Deep Learning with Label Differential Privacy](https://mlsanity.com/paper/2102.06062/) (2021)
- [VAFL: a Method of Vertical Asynchronous Federated Learning](https://mlsanity.com/paper/2007.06081/) (2020)
- [Decentralized Training of Foundation Models in Heterogeneous Environments](https://mlsanity.com/paper/2206.01288/) (2022)
- [FederatedScope: A Flexible Federated Learning Platform for Heterogeneity](https://mlsanity.com/paper/2204.05011/) (2022)
- [Antipodes of Label Differential Privacy: PATE and ALIBI](https://mlsanity.com/paper/2106.03408/) (2021)
- [DeepSecure: Scalable Provably-Secure Deep Learning](https://mlsanity.com/paper/1705.08963/) (2017)
- [Multi-Participant Multi-Class Vertical Federated Learning](https://mlsanity.com/paper/2001.11154/) (2020)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://mlsanity.com/paper/1810.08553/) (2018)
- [Secure Bilevel Asynchronous Vertical Federated Learning with Backward Updating](https://mlsanity.com/paper/2103.00958/) (2021)
- [Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data](https://mlsanity.com/paper/2008.06197/) (2020)
- [Federated Transfer Learning: concept and applications](https://mlsanity.com/paper/2010.15561/) (2020)
- [Towards Communication-efficient Vertical Federated Learning Training via Cache-enabled Local Updates](https://mlsanity.com/paper/2207.14628/) (2022)
- [Learning Privately over Distributed Features: An ADMM Sharing Approach](https://mlsanity.com/paper/1907.07735/) (2019)
- [Hybrid Federated Learning: Algorithms and Implementation](https://mlsanity.com/paper/2012.12420/) (2020)
- [Towards Linear Algebra over Normalized Data](https://mlsanity.com/paper/1612.07448/) (2016)
- [A Layered Aggregate Engine for Analytics Workloads](https://mlsanity.com/paper/1906.08687/) (2019)
- [Federated Learning of Medical Concepts Embedding using BEHRT](https://mlsanity.com/paper/2305.13052/) (2023)
- [FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging](https://mlsanity.com/paper/2204.00976/) (2022)

## Abstract (arXiv)

Current federated learning (FL) approaches view decentralized training data as a single table, divided among participants either horizontally (by rows) or vertically (by columns). However, these approaches are inadequate for handling distributed relational tables across databases. This scenario requires intricate SQL operations like joins and unions to obtain the training data, which is either costly or restricted by privacy concerns. This raises the question: can we directly run FL on distributed relational tables? In this paper, we formalize this problem as relational federated learning (RFL). We propose TablePuppet, a generic framework for RFL that decomposes the learning process into two steps: (1) learning over join (LoJ) followed by (2) learning over union (LoU). In a nutshell, LoJ pushes learning down onto the vertical tables being joined, and LoU further pushes learning down onto the horizontal partitions of each vertical table. TablePuppet incorporates computation/communication optimizations to deal with the duplicate tuples introduced by joins, as well as differential privacy (DP) to protect against both feature and label leakages. We demonstrate the efficiency of TablePuppet in combination with two widely-used ML training algorithms, stochastic gradient descent (SGD) and alternating direction method of multipliers (ADMM), and compare their computation/communication complexity. We evaluate the SGD/ADMM algorithms developed atop TablePuppet by training diverse ML models. Our experimental results show that TablePuppet achieves model accuracy comparable to the centralized baselines running directly atop the SQL results. Moreover, ADMM takes less communication time than SGD to converge to similar model accuracy.