Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries

Hina Dixit; Punit Kumar; Irene Tenison; Nevasini Sasikumar

cs.CRJun 1, 2026

Echelon: Auditable Aggregate-Only Language-Model Adaptation Across Privacy Boundaries

Authors: Hina Dixit, Punit Kumar, Irene Tenison, Nevasini Sasikumar

Organizations: Decompute Inc., Cupertino

Abstract

Cross-organization language-model adaptation increasingly faces hard governance constraints: in many deployments, device-level model state-parameters, activations, optimizer state, and per-device updates-cannot be exported outside an administrative boundary. Existing distributed and federated stacks typically assume cross-site model exchange and then retrofit privacy mechanisms, which complicates compliance and makes auditing brittle. We present Echelon, a boundary-first training architecture that enforces device-level model-state non-export as a systems invariant. Devices train locally inside each boundary; the only cross-boundary payloads are securely aggregated boundary-level deltas plus O(1) coordination metadata, exposed through a concrete audit surface. Restricting exchange to aggregates changes the optimization problem: the system must remain stable under WAN delay, heterogeneous participation, churn, and non-IID data even though the global plane never sees per-device updates. Echelon combines buffered semi-asynchronous secure aggregation, staleness-aware weighting, participation windows, proximal local objectives, and a drift-aware outer synchronization controller. In 1B-parameter LoRA adaptation across M= 2 boundaries, a budget-matched contest over three seeds (24.88M tokens) reaches validation loss 3.887 +/-0.010 and is best or tied-best among tuned low-communication baselines under fixed-token, fixed-bytes, fixed-wall-clock, and fixed-sync-count budgets. In OpenWebText stress tests, Echelon sustains 2,139-2,176 tokens/s across evaluated WAN and non-IID treatments, Echelon-DA improves time-to-target under WAN latency relative to a privacy-parityDiLoCo+SA baseline, and quality degrades by at most 2.2% under 200ms emulated latency or severe non-IID partitioning.

Explore similar work

May 7, 2026cs.LG

Adaptive Selection of LoRA Components in Privacy-Preserving Federated Learning

Differentially private federated fine-tuning of large models with LoRA suffers from aggregation error caused by LoRA's multiplicative structure, which is further amplified by DP noise and degrades both stability and accuracy. Existing remedies apply a single update mode uniformly across all layers and all communication rounds (or alternate them on a fixed schedule), ignoring both the structural asymmetry between the two LoRA factors and the round-wise dynamics of training. We propose AS-LoRA, an adaptive framework defined by three axes (i) layer-wise freedom, in which each layer independently selects its active component, (ii) round-wise adaptivity, in which the selection updates over communication rounds, and (iii) a curvature-aware score derived from a second-order approximation of the loss. Theoretically, AS-LoRA eliminates the reconstruction-error floor of layer-tied schedules, accelerates convergence, implicitly biases solutions toward flatter minima, and incurs no additional privacy cost. Across GLUE, SQuAD, CIFAR-100, and Tiny-ImageNet under strict DP budgets and non-IID partitions, AS-LoRA improves over the federated LoRA baselines by up to

+7.5

pp on GLUE and

+12.5

pp on MNLI-mm for example, while matching or exceeding SVD-based aggregation methods at

33\text{--}180 \times

lower aggregation cost and with negligible communication overhead. Code for the proposed method is available at https://anonymous.4open.science/r/as_lora-F75F/.

Myoungjun Kim, Sangwoo Park, Yoseob Han +1

Federated Learning Low-Rank Adaptation Imagenet

Ask PDF

May 11, 2026cs.LG

DP-LAC: Lightweight Adaptive Clipping for Differentially Private Federated Fine-tuning of Language Models

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 sensitive information through client-provided gradients. Differentially private stochastic gradient descent (DP-SGD) mitigates this risk by clipping each client's contribution to a threshold

C

and adding noise proportional to

C

. Existing adaptive clipping techniques dynamically adjust

C

but demand tedious hyperparameter tuning, which can erode the privacy budget. In this paper, we introduce DP-LAC, a method that first estimates an initial clipping threshold within an order of magnitude of the optimum using private histogram estimation, and then adapts this threshold during training without consuming additional privacy budget or introducing new hyperparameters. Empirical results show that DP-LAC outperforms both state-of-the-art adaptive clipping methods and vanilla DP-SGD, achieving an average accuracy gain of

6.6\%

Haaris Mehmood, Jie Xu, Karthikeyan Saravanan +2

Federated Learning Differential Privacy Gradient Clipping Stochastic Gradient Descent Large Language

Ask PDF

Aug 8, 2026cs.CV

EFFEKT: Efficient Federated Knowledge Transfer to Foundation Models

Recent data protection laws have accelerated the adoption of Federated Learning (FL) for privacy-preserving decentralized training. Nevertheless, increasing model sizes impose substantial computational demands on client devices, limiting FL applicability in resource-constrained settings. We introduce a novel multi-domain federated learning framework in which lightweight client-side proxy models collaborate with a server-side Foundation Model (FM) to learn new concepts without sharing private data. Our approach, EFFEKT, enables efficient server-side training of domain-specific LoRA adapters while preserving feature-space alignment between the FM and proxy extractors via novel bi-directional cross-distillation strategies. Experiments on multiple real-world datasets and deployments on low-power edge devices demonstrate improvements over state-of-the-art baselines in most considered domains while maintaining lightweight computation at the client side.

Matteo Caligiuri, Francesco Barbato, Pietro Zanuttigh +1

Federated Learning Wireless Foundation Models Low-Rank Adaptation Adapters Multi-Domain Foundation Model Private Data

Ask PDF