Agnostic Federated Learning (1902.00146v1)

Abstract: A key learning scenario in large-scale applications is that of federated learning, where a centralized model is trained based on data originating from a large number of clients. We argue that, with the existing training and inference, federated models can be biased towards different clients. Instead, we propose a new framework of agnostic federated learning, where the centralized model is optimized for any target distribution formed by a mixture of the client distributions. We further show that this framework naturally yields a notion of fairness. We present data-dependent Rademacher complexity guarantees for learning with this objective, which guide the definition of an algorithm for agnostic federated learning. We also give a fast stochastic optimization algorithm for solving the corresponding optimization problem, for which we prove convergence bounds, assuming a convex loss function and hypothesis set. We further empirically demonstrate the benefits of our approach in several datasets. Beyond federated learning, our framework and algorithm can be of interest to other learning scenarios such as cloud computing, domain adaptation, drifting, and other contexts where the training and test distributions do not coincide.

• The paper introduces the AFL framework that optimizes a central model for any mixture of client distributions rather than a fixed uniform distribution.
• It presents a robust stochastic optimization algorithm with convergence guarantees to ensure balanced performance across diverse client groups.
• The work provides theoretical Rademacher complexity bounds and experimental evidence showing improved accuracy and fairness over traditional methods.

Agnostic Federated Learning

The paper "Agnostic Federated Learning" by Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh introduces a novel framework for federated learning that addresses key limitations in existing approaches. The authors propose a paradigm shift towards agnostic federated learning (AFL), aiming to optimize a centralized model for any possible target distribution formed by a mixture of client distributions, rather than tailoring it to a specific distribution that may not represent the test-time scenario.

Overview

Federated learning enables the training of a centralized model using data distributed across numerous clients. Traditional federated learning methods typically aggregate local models or updates from subsets of clients, often assuming that the uniform distribution of these updates is a suitable representation of the real-world target distribution. However, in practice, the sample sizes and participation likelihoods of different clients can vary significantly due to network connectivity issues or device-specific factors, leading to potential biases in the centralized model.

To counter these challenges, the authors introduce the AFL framework, which optimizes the model for any mixture of the client distributions. This approach not only addresses the issue of discrepancy between training and target distributions but also naturally promotes fairness, thus reducing biases towards specific client groups.

Theoretical Contributions

The AFL framework's principal contributions include:

1. Definition and Motivation: The paper precisely formulates the AFL problem and demonstrates with examples that optimizing for uniform distributions can be suboptimal or even detrimental compared to the proposed agnostic approach. This is exemplified through scenarios where substantial variations between client distributions lead to significant performance losses.
2. Fairness Considerations: The AFL framework inherently supports a fairness notion termed "good-intent fairness", which ensures that the model performs equitably across various protected categories or client domains. This contrasts with standard methods that may inadvertently favor dominant client categories.
3. Generalization Bounds: The authors derive data-dependent Rademacher complexity bounds to provide theoretical generalization guarantees for AFL. These bounds consider the empirical loss and a skewness term, which controls the discrepancy between the empirical and true underlying distributions.
4. Optimization Algorithm: The paper presents a robust stochastic optimization algorithm tailored for AFL, incorporating regularization to manage the skewness of the client distribution mixtures. The convergence guarantees of this algorithm are rigorously proven, making it both practical and theoretically sound.

Experimental Validation

The empirical benefits of the AFL approach are demonstrated through experiments on various datasets, showcasing its superiority over traditional federated learning methods. The experiments confirm that AFL models achieve higher accuracy and better performance consistency across diverse client distributions, emphasizing the framework's practical utility.

Implications and Future Work

The implications of the AFL framework extend beyond federated learning:

• AI Fairness: The good-intent fairness principle introduced by AFL could be adapted to other learning paradigms where fairness and bias are critical concerns.
• Domain Adaptation and Drift: AFL's robustness against distribution mismatches makes it relevant for scenarios involving domain adaptation and distribution drifts, prevalent in fields like cloud computing and decentralized learning environments.

Future developments could explore adaptive mechanisms to dynamically determine the most impactful client distributions during training, further enhancing the overall performance and fairness of the AFL approach.

In conclusion, the paper by Mohri, Sivek, and Suresh presents a significant advance in federated learning by introducing the agnostic federated learning framework. Its rigorous theoretical foundations, coupled with practical optimization solutions and empirical validation, underscore the approach's effectiveness and adaptability in real-world scenarios.