FACT: Federated Adversarial Cross Training (2306.00607v2)

Abstract: Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i.d. data. A particularly challenging scenario is the federated model adaptation to a target client without access to annotated data. We propose Federated Adversarial Cross Training (FACT), which uses the implicit domain differences between source clients to identify domain shifts in the target domain. In each round of FL, FACT cross initializes a pair of source clients to generate domain specialized representations which are then used as a direct adversary to learn a domain invariant data representation. We empirically show that FACT outperforms state-of-the-art federated, non-federated and source-free domain adaptation models on three popular multi-source-single-target benchmarks, and state-of-the-art Unsupervised Domain Adaptation (UDA) models on single-source-single-target experiments. We further study FACT's behavior with respect to communication restrictions and the number of participating clients.

• The paper introduces FACT, a novel method for unsupervised federated domain adaptation that leverages inter-domain differences from multiple sources to improve model performance on an unlabeled target domain.
• FACT employs a three-phase approach involving cross-initialization from selected source clients, fine-tuning of classifiers with a frozen feature extractor, and minimizing inter-domain distance using an adversarial strategy.
• Empirical results on datasets like Digit-Five and Office show that FACT outperforms existing benchmarks (e.g., FADA, FedKA) in federated settings, demonstrating robustness even under communication constraints and varying client numbers.

Federated Adversarial Cross Training: A Technical Overview

The paper introduces Federated Adversarial Cross Training (FACT), a novel approach for unsupervised federated domain adaptation, characterized by its focus on adapting models across multiple source domains to a target domain without labeled data access. The challenge of non-identically independently distributed (non-i.i.d.) data in federated learning scenarios is well recognized, especially as it pertains to ensuring the robustness and efficacy of model training across disparate data silos without compromising privacy.

Approach and Methodology

The authors' contribution is framed within the context of Federated Learning (FL), an architecture that permits multi-party collaborative model training without directly sharing the data, which is particularly critical for fields such as precision medicine. FACT leverages inter-domain differences from multiple sources to address the domain shift problem between these source domains and an unlabeled target domain. The technique can be described in the following phases:

1. Source Training and Cross-Initialization: In this phase, two randomly selected source clients undergo model training with local data using a standard cross-entropy loss to ensure domain-specific learning. Following this, model parameters are averaged in a federated manner.
2. Fine-Tuning: The model's feature extractor is frozen and classifiers are fine-tuned against the updated representation to maintain performance enhancements attributed to domain-specific characteristics. This is crucial for facilitating domain-invariant feature extraction.
3. Inter-Domain Distance Minimization: The technique capitalizes on the discrepancies between predictions made by domain-specific classifiers as proxies for measuring and reducing domain shifts at the target site. FACT's adversarial strategy, the Inter-Domain Distance (IDD) loss, ensures convergence towards domain-invariant representations.

Empirical Evaluation and Results

FACT's empirical evaluations on datasets such as Digit-Five, Office, and Office-Caltech10 illustrate its capability over both federated and non-federated settings. Especially notable is its enhanced performance on difficult target domains such as SVHN and MNISTM within the Digit-Five dataset. The performance improvements are apparent when FACT's results are measured against established benchmarks like FADA, and FedKA. The fact that FACT outperforms these models even under communication constraints and varying client numbers underscores its robustness.

Implications and Future Directions

The introduction of FACT heralds promising functionality in federated learning environments fraught with domain shifts. The method's potential to circumvent negative transfer—a common problem in federated learning—presents opportunities for robust application across diverse industries where data privacy and heterogeneity are central concerns. By demonstrating competitive performance even under scenarios violating their own assumptions, such as single-source domain adaptation, the authors lay the groundwork for future advancements.

A pertinent avenue for future work is formalizing theoretical guarantees on scenarios conducive to significant domain adaptation. Additionally, optimizing model communication efficiency while maintaining performance is a critical area for further development, given the potential overheads associated with transmitting large model parameters in federated architectures. Overall, FACT's adaptable strategy for multi-source domain adaptation offers a valuable blueprint for advancing the frontiers of federated AI systems.

In conclusion, this paper presents a structured approach to federated domain adaptation that contributes significantly to understanding and leveraging cross-domain variability for enhanced model adaptation. This work may inspire further advancements in federated learning applications, especially in data-sensitive and heterogeneous areas such as healthcare and finance.