7 papers
DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
Haaris Mehmood, Giorgos Tatsis, Dimitrios Alexopoulos +4
Federated learning enables collaborative model training across distributed clients, yet vanilla FL exposes client updates to the central server. Secure-aggregation schemes protect…
DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models
Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2
Federated learning (FL) enables the collaborative training of large-scale language models (LLMs) across edge devices while keeping user data on-device. However, FL still exposes se…
Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
Jie Xu, Haaris Mehmood, Rogier Van Dalen +2
Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user information and thus in practice,…
ValSub: Subsampling Validation Data to Mitigate Forgetting during ASR Personalization
Haaris Mehmood, Karthikeyan Saravanan, Pablo Peso Parada +5
Automatic Speech Recognition (ASR) is widely used within consumer devices such as mobile phones. Recently, personalization or on-device model fine-tuning has shown that adaptation…
DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation
Jie Xu, Karthikeyan Saravanan, Rogier van Dalen +3
Federated learning (FL) allows clients to collaboratively train a global model without sharing their local data with a server. However, clients' contributions to the server can sti…
Consistency Based Unsupervised Self-training For ASR Personalisation
Jisi Zhang, Vandana Rajan, Haaris Mehmood +7
On-device Automatic Speech Recognition (ASR) models trained on speech data of a large population might underperform for individuals unseen during training. This is due to a domain…
FedNST: Federated Noisy Student Training for Automatic Speech Recognition
Haaris Mehmood, Agnieszka Dobrowolska, Karthikeyan Saravanan +1
Federated Learning (FL) enables training state-of-the-art Automatic Speech Recognition (ASR) models on user devices (clients) in distributed systems, hence preventing transmission…