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Found 12 papers, 5 papers with code
Fed-HeLLo: Efficient Federated Foundation Model Fine-Tuning with Heterogeneous LoRA Allocation
By co-designing the proposed HLA strategies, we incorporate both the dynamic and intrinsic layer importance into the design of our HLA strategy.
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raising concerns about data scarcity and the lack of access to domain-specific, sensitive information.
Traceable Black-box Watermarks for Federated Learning
Based on the problem, we propose a novel server-side watermarking method, $\mathbf{TraMark}$, which creates a traceable watermarked model for each client, enabling verification of model leakage in black-box settings.
ROMA: a Read-Only-Memory-based Accelerator for QLoRA-based On-Device LLM
In this work, we propose ROMA, a QLoRA accelerator with a hybrid storage architecture that uses ROM for quantized base models and SRAM for LoRA weights and KV cache.
Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
The distributed nature of training makes Federated Learning (FL) vulnerable to backdoor attacks, where malicious model updates aim to compromise the global model's performance on specific tasks.
Identify Backdoored Model in Federated Learning via Individual Unlearning
Inspired by this, we propose MASA, a method that utilizes individual unlearning on local models to identify malicious models in FL.
Fed-pilot: Optimizing LoRA Allocation for Efficient Federated Fine-Tuning with Heterogeneous Clients
Federated Learning enables the fine-tuning of foundation models (FMs) across distributed clients for specific tasks; however, its scalability is limited by the heterogeneity of client memory capacities.
Federated Learning for Smart Grid: A Survey on Applications and Potential Vulnerabilities
Unlike traditional surveys addressing security issues in centralized machine learning methods for SG systems, this survey is the first to specifically examine the applications and security concerns unique to FL-based SG systems.
Achieving Byzantine-Resilient Federated Learning via Layer-Adaptive Sparsified Model Aggregation
To address these challenges, we propose the Layer-Adaptive Sparsified Model Aggregation (LASA) approach, which combines pre-aggregation sparsification with layer-wise adaptive aggregation to improve robustness.
Byzantine-Robust Federated Learning with Variance Reduction and Differential Privacy
Federated learning (FL) is designed to preserve data privacy during model training, where the data remains on the client side (i. e., IoT devices), and only model updates of clients are shared iteratively for collaborative learning.
Distractor-Aware Fast Tracking via Dynamic Convolutions and MOT Philosophy
A practical long-term tracker typically contains three key properties, i. e. an efficient model design, an effective global re-detection strategy and a robust distractor awareness mechanism.
Attention-based Supply-Demand Prediction for Autonomous Vehicles
Short term temporal dependency is captured with LSTM.
