With the increasing availability of Foundation Models, federated tuning has garnered attention in the field of federated learning, utilizing data and computation resources from multiple clients to collaboratively fine-tune foundation models. However, in real-world federated scenarios, there often exist a multitude of heterogeneous clients with varying computation and communication resources, rendering them incapable of supporting the entire model fine-tuning process. In response to this challenge, we propose a novel federated tuning algorithm, FedRA. The implementation of FedRA is straightforward and can be seamlessly integrated into any transformer-based model without the need for further modification to the original model. Specifically, in each communication round, FedRA randomly generates an allocation matrix. For resource-constrained clients, it reorganizes a small number of layers from the original model based on the allocation matrix and fine-tunes using LoRA. Subsequently, the server aggregates the updated LoRA parameters from the clients according to the current allocation matrix into the corresponding layers of the original model. It is worth noting that FedRA also supports scenarios where none of the clients can support the entire global model, which is an impressive advantage. We conduct experiments on two large-scale image datasets, DomainNet and NICO++, under various non-iid settings. The results demonstrate that FedRA outperforms the compared methods significantly. The source code is available at \url{https://github.com/leondada/FedRA}.

Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework. However, classical FL faces serious security and robustness problem, e.g., malicious clients can poison model updates and at the same time claim large quantities to amplify the impact of their model updates in the model aggregation. Existing defense methods for FL, while all handling malicious model updates, either treat all quantities benign or simply ignore/truncate the quantities of all clients. The former is vulnerable to quantity-enhanced attack, while the latter leads to sub-optimal performance since the local data on different clients is usually in significantly different sizes. In this paper, we propose a robust quantity-aware aggregation algorithm for federated learning, called FedRA, to perform the aggregation with awareness of local data quantities while being able to defend against quantity-enhanced attacks. More specifically, we propose a method to filter malicious clients by jointly considering the uploaded model updates and data quantities from different clients, and performing quantity-aware weighted averaging on model updates from remaining clients. Moreover, as the number of malicious clients participating in the federated learning may dynamically change in different rounds, we also propose a malicious client number estimator to predict how many suspicious clients should be filtered in each round. Experiments on four public datasets demonstrate the effectiveness of our FedRA method in defending FL against quantity-enhanced attacks.