# Towards Causal Federated Learning For Enhanced Robustness and Privacy

arXiv: https://arxiv.org/abs/2104.06557 · HTML: https://mlsanity.com/paper/2104.06557/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Invariant Risk Minimization](https://mlsanity.com/paper/1907.02893/) (2019)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Differentially Private Federated Learning: A Client Level Perspective](https://mlsanity.com/paper/1712.07557/) (2017)
- [Domain Generalization using Causal Matching](https://mlsanity.com/paper/2006.07500/) (2020)
- [Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning](https://mlsanity.com/paper/1812.00910/) (2018)
- [Learning Differentially Private Recurrent Language Models](https://mlsanity.com/paper/1710.06963/) (2017)
- [Alleviating Privacy Attacks via Causal Learning](https://mlsanity.com/paper/1909.12732/) (2019)

## Built on by (most cited first, 14 of 14)

- [Federated Domain Generalization: A Survey](https://mlsanity.com/paper/2306.01334/) (2023)
- [Gradient Masked Averaging for Federated Learning](https://mlsanity.com/paper/2201.11986/) (2022)
- [Mitigating Data Heterogeneity in Federated Learning with Data Augmentation](https://mlsanity.com/paper/2206.09979/) (2022)
- [Distribution inference risks: Identifying and mitigating sources of leakage](https://mlsanity.com/paper/2209.08541/) (2022)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Domain Generalization -- A Causal Perspective](https://mlsanity.com/paper/2209.15177/) (2022)
- [FL Games: A Federated Learning Framework for Distribution Shifts](https://mlsanity.com/paper/2211.00184/) (2022)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal](https://mlsanity.com/paper/2110.09695/) (2021)
- [Deep Causal Learning: Representation, Discovery and Inference](https://mlsanity.com/paper/2211.03374/) (2022)
- [A Federated Approach to Predicting Emojis in Hindi Tweets](https://mlsanity.com/paper/2211.06401/) (2022)
- [The Connection between Out-of-Distribution Generalization and Privacy of ML Models](https://mlsanity.com/paper/2110.03369/) (2021)
- [FL Games: A federated learning framework for distribution shifts](https://mlsanity.com/paper/2205.11101/) (2022)
- [Global Layers: Non-IID Tabular Federated Learning](https://mlsanity.com/paper/2305.19290/) (2023)

## Abstract (arXiv)

Federated Learning is an emerging privacy-preserving distributed machine learning approach to building a shared model by performing distributed training locally on participating devices (clients) and aggregating the local models into a global one. As this approach prevents data collection and aggregation, it helps in reducing associated privacy risks to a great extent. However, the data samples across all participating clients are usually not independent and identically distributed (non-iid), and Out of Distribution(OOD) generalization for the learned models can be poor. Besides this challenge, federated learning also remains vulnerable to various attacks on security wherein a few malicious participating entities work towards inserting backdoors, degrading the generated aggregated model as well as inferring the data owned by participating entities. In this paper, we propose an approach for learning invariant (causal) features common to all participating clients in a federated learning setup and analyze empirically how it enhances the Out of Distribution (OOD) accuracy as well as the privacy of the final learned model.